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Research Findings – Types Examples and Writing Guide

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Research Findings

Definition:

Research findings refer to the results obtained from a study or investigation conducted through a systematic and scientific approach. These findings are the outcomes of the data analysis, interpretation, and evaluation carried out during the research process.

Types of Research Findings

There are two main types of research findings:

Qualitative Findings

Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants, themes that emerge from the data, and descriptions of experiences and phenomena.

Quantitative Findings

Quantitative research is a research method that uses numerical data and statistical analysis to measure and quantify a phenomenon or behavior. Quantitative findings include numerical data such as mean, median, and mode, as well as statistical analyses such as t-tests, ANOVA, and regression analysis. These findings are often presented in tables, graphs, or charts.

Both qualitative and quantitative findings are important in research and can provide different insights into a research question or problem. Combining both types of findings can provide a more comprehensive understanding of a phenomenon and improve the validity and reliability of research results.

Parts of Research Findings

Research findings typically consist of several parts, including:

• Introduction: This section provides an overview of the research topic and the purpose of the study.
• Literature Review: This section summarizes previous research studies and findings that are relevant to the current study.
• Methodology : This section describes the research design, methods, and procedures used in the study, including details on the sample, data collection, and data analysis.
• Results : This section presents the findings of the study, including statistical analyses and data visualizations.
• Discussion : This section interprets the results and explains what they mean in relation to the research question(s) and hypotheses. It may also compare and contrast the current findings with previous research studies and explore any implications or limitations of the study.
• Conclusion : This section provides a summary of the key findings and the main conclusions of the study.
• Recommendations: This section suggests areas for further research and potential applications or implications of the study’s findings.

How to Write Research Findings

Writing research findings requires careful planning and attention to detail. Here are some general steps to follow when writing research findings:

• Organize your findings: Before you begin writing, it’s essential to organize your findings logically. Consider creating an outline or a flowchart that outlines the main points you want to make and how they relate to one another.
• Use clear and concise language : When presenting your findings, be sure to use clear and concise language that is easy to understand. Avoid using jargon or technical terms unless they are necessary to convey your meaning.
• Use visual aids : Visual aids such as tables, charts, and graphs can be helpful in presenting your findings. Be sure to label and title your visual aids clearly, and make sure they are easy to read.
• Use headings and subheadings: Using headings and subheadings can help organize your findings and make them easier to read. Make sure your headings and subheadings are clear and descriptive.
• Interpret your findings : When presenting your findings, it’s important to provide some interpretation of what the results mean. This can include discussing how your findings relate to the existing literature, identifying any limitations of your study, and suggesting areas for future research.
• Be precise and accurate : When presenting your findings, be sure to use precise and accurate language. Avoid making generalizations or overstatements and be careful not to misrepresent your data.
• Edit and revise: Once you have written your research findings, be sure to edit and revise them carefully. Check for grammar and spelling errors, make sure your formatting is consistent, and ensure that your writing is clear and concise.

Research Findings Example

Following is a Research Findings Example sample for students:

Title: The Effects of Exercise on Mental Health

Sample : 500 participants, both men and women, between the ages of 18-45.

Methodology : Participants were divided into two groups. The first group engaged in 30 minutes of moderate intensity exercise five times a week for eight weeks. The second group did not exercise during the study period. Participants in both groups completed a questionnaire that assessed their mental health before and after the study period.

Findings : The group that engaged in regular exercise reported a significant improvement in mental health compared to the control group. Specifically, they reported lower levels of anxiety and depression, improved mood, and increased self-esteem.

Conclusion : Regular exercise can have a positive impact on mental health and may be an effective intervention for individuals experiencing symptoms of anxiety or depression.

Applications of Research Findings

Research findings can be applied in various fields to improve processes, products, services, and outcomes. Here are some examples:

• Healthcare : Research findings in medicine and healthcare can be applied to improve patient outcomes, reduce morbidity and mortality rates, and develop new treatments for various diseases.
• Education : Research findings in education can be used to develop effective teaching methods, improve learning outcomes, and design new educational programs.
• Technology : Research findings in technology can be applied to develop new products, improve existing products, and enhance user experiences.
• Business : Research findings in business can be applied to develop new strategies, improve operations, and increase profitability.
• Public Policy: Research findings can be used to inform public policy decisions on issues such as environmental protection, social welfare, and economic development.
• Social Sciences: Research findings in social sciences can be used to improve understanding of human behavior and social phenomena, inform public policy decisions, and develop interventions to address social issues.
• Agriculture: Research findings in agriculture can be applied to improve crop yields, develop new farming techniques, and enhance food security.
• Sports : Research findings in sports can be applied to improve athlete performance, reduce injuries, and develop new training programs.

When to use Research Findings

Research findings can be used in a variety of situations, depending on the context and the purpose. Here are some examples of when research findings may be useful:

• Decision-making : Research findings can be used to inform decisions in various fields, such as business, education, healthcare, and public policy. For example, a business may use market research findings to make decisions about new product development or marketing strategies.
• Problem-solving : Research findings can be used to solve problems or challenges in various fields, such as healthcare, engineering, and social sciences. For example, medical researchers may use findings from clinical trials to develop new treatments for diseases.
• Policy development : Research findings can be used to inform the development of policies in various fields, such as environmental protection, social welfare, and economic development. For example, policymakers may use research findings to develop policies aimed at reducing greenhouse gas emissions.
• Program evaluation: Research findings can be used to evaluate the effectiveness of programs or interventions in various fields, such as education, healthcare, and social services. For example, educational researchers may use findings from evaluations of educational programs to improve teaching and learning outcomes.
• Innovation: Research findings can be used to inspire or guide innovation in various fields, such as technology and engineering. For example, engineers may use research findings on materials science to develop new and innovative products.

Purpose of Research Findings

The purpose of research findings is to contribute to the knowledge and understanding of a particular topic or issue. Research findings are the result of a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques.

The main purposes of research findings are:

• To generate new knowledge : Research findings contribute to the body of knowledge on a particular topic, by adding new information, insights, and understanding to the existing knowledge base.
• To test hypotheses or theories : Research findings can be used to test hypotheses or theories that have been proposed in a particular field or discipline. This helps to determine the validity and reliability of the hypotheses or theories, and to refine or develop new ones.
• To inform practice: Research findings can be used to inform practice in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners to make informed decisions and improve outcomes.
• To identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research.
• To contribute to policy development: Research findings can be used to inform policy development in various fields, such as environmental protection, social welfare, and economic development. By providing evidence-based recommendations, research findings can help policymakers to develop effective policies that address societal challenges.

Characteristics of Research Findings

Research findings have several key characteristics that distinguish them from other types of information or knowledge. Here are some of the main characteristics of research findings:

• Objective : Research findings are based on a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques. As such, they are generally considered to be more objective and reliable than other types of information.
• Empirical : Research findings are based on empirical evidence, which means that they are derived from observations or measurements of the real world. This gives them a high degree of credibility and validity.
• Generalizable : Research findings are often intended to be generalizable to a larger population or context beyond the specific study. This means that the findings can be applied to other situations or populations with similar characteristics.
• Transparent : Research findings are typically reported in a transparent manner, with a clear description of the research methods and data analysis techniques used. This allows others to assess the credibility and reliability of the findings.
• Peer-reviewed: Research findings are often subject to a rigorous peer-review process, in which experts in the field review the research methods, data analysis, and conclusions of the study. This helps to ensure the validity and reliability of the findings.
• Reproducible : Research findings are often designed to be reproducible, meaning that other researchers can replicate the study using the same methods and obtain similar results. This helps to ensure the validity and reliability of the findings.

Advantages of Research Findings

Research findings have many advantages, which make them valuable sources of knowledge and information. Here are some of the main advantages of research findings:

• Evidence-based: Research findings are based on empirical evidence, which means that they are grounded in data and observations from the real world. This makes them a reliable and credible source of information.
• Inform decision-making: Research findings can be used to inform decision-making in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners and policymakers to make informed decisions and improve outcomes.
• Identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research. This contributes to the ongoing development of knowledge in various fields.
• Improve outcomes : Research findings can be used to develop and implement evidence-based practices and interventions, which have been shown to improve outcomes in various fields, such as healthcare, education, and social services.
• Foster innovation: Research findings can inspire or guide innovation in various fields, such as technology and engineering. By providing new information and understanding of a particular topic, research findings can stimulate new ideas and approaches to problem-solving.
• Enhance credibility: Research findings are generally considered to be more credible and reliable than other types of information, as they are based on rigorous research methods and are subject to peer-review processes.

Limitations of Research Findings

While research findings have many advantages, they also have some limitations. Here are some of the main limitations of research findings:

• Limited scope: Research findings are typically based on a particular study or set of studies, which may have a limited scope or focus. This means that they may not be applicable to other contexts or populations.
• Potential for bias : Research findings can be influenced by various sources of bias, such as researcher bias, selection bias, or measurement bias. This can affect the validity and reliability of the findings.
• Ethical considerations: Research findings can raise ethical considerations, particularly in studies involving human subjects. Researchers must ensure that their studies are conducted in an ethical and responsible manner, with appropriate measures to protect the welfare and privacy of participants.
• Time and resource constraints : Research studies can be time-consuming and require significant resources, which can limit the number and scope of studies that are conducted. This can lead to gaps in knowledge or a lack of research on certain topics.
• Complexity: Some research findings can be complex and difficult to interpret, particularly in fields such as science or medicine. This can make it challenging for practitioners and policymakers to apply the findings to their work.
• Lack of generalizability : While research findings are intended to be generalizable to larger populations or contexts, there may be factors that limit their generalizability. For example, cultural or environmental factors may influence how a particular intervention or treatment works in different populations or contexts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

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How To Write the Findings Section of a Research Paper

Posted by Rene Tetzner | Sep 2, 2021 | Paper Writing Advice | 0 |

How To Write the Findings Section of a Research Paper Each research project is unique, so it is natural for one researcher to make use of somewhat different strategies than another when it comes to designing and writing the section of a research paper dedicated to findings. The academic or scientific discipline of the research, the field of specialisation, the particular author or authors, the targeted journal or other publisher and the editor making the decisions about publication can all have a significant impact. The practical steps outlined below can be effectively applied to writing about the findings of most advanced research, however, and will prove especially helpful for early-career scholars who are preparing a research paper for a first publication.

Step 1 : Consult the guidelines or instructions that the targeted journal (or other publisher) provides for authors and read research papers it has already published, particularly ones similar in topic, methods or results to your own. The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches. Watch particularly for length limitations and restrictions on content. Interpretation, for instance, is usually reserved for a later discussion section, though not always – qualitative research papers often combine findings and interpretation. Background information and descriptions of methods, on the other hand, almost always appear in earlier sections of a research paper. In most cases it is appropriate in a findings section to offer basic comparisons between the results of your study and those of other studies, but knowing exactly what the journal wants in the report of research findings is essential. Learning as much as you can about the journal’s aims and scope as well as the interests of its readers is invaluable as well.

Step 2 : Reflect at some length on your research results in relation to the journal’s requirements while planning the findings section of your paper. Choose for particular focus experimental results and other research discoveries that are particularly relevant to your research questions and objectives, and include them even if they are unexpected or do not support your ideas and hypotheses. Streamline and clarify your report, especially if it is long and complex, by using subheadings that will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Consider appendices for raw data that might interest specialists but prove too long or distracting for other readers. The opening paragraph of a findings section often restates research questions or aims to refocus the reader’s attention, and it is always wise to summarise key findings at the end of the section, providing a smooth intellectual transition to the interpretation and discussion that follows in most research papers. There are many effective ways in which to organise research findings. The structure of your findings section might be determined by your research questions and hypotheses or match the arrangement of your methods section. A chronological order or hierarchy of importance or meaningful grouping of main themes or categories might prove effective. It may be best to present all the relevant findings and then explain them and your analysis of them, or explaining the results of each trial or test immediately after reporting it may render the material clearer and more comprehensible for your readers. Keep your audience, your most important evidence and your research goals in mind.

Step 3 : Design effective visual presentations of your research results to enhance the textual report of your findings. Tables of various styles and figures of all kinds such as graphs, maps and photos are used in reporting research findings, but do check the journal guidelines for instructions on the number of visual aids allowed, any required design elements and the preferred formats for numbering, labelling and placement in the manuscript. As a general rule, tables and figures should be numbered according to first mention in the main text of the paper, and each one should be clearly introduced and explained at least briefly in that text so that readers know what is presented and what they are expected to see in a particular visual element. Tables and figures should also be self-explanatory, however, so their design should include all definitions and other information necessary for a reader to understand the findings you intend to show without returning to your text. If you construct your tables and figures before drafting your findings section, they can serve as focal points to help you tell a clear and informative story about your findings and avoid unnecessary repetition. Some authors will even work on tables and figures before organising the findings section (Step 2), which can be an extremely effective approach, but it is important to remember that the textual report of findings remains primary. Visual aids can clarify and enrich the text, but they cannot take its place.

Step 4 : Write your findings section in a factual and objective manner. The goal is to communicate information – in some cases a great deal of complex information – as clearly, accurately and precisely as possible, so well-constructed sentences that maintain a simple structure will be far more effective than convoluted phrasing and expressions. The active voice is often recommended by publishers and the authors of writing manuals, and the past tense is appropriate because the research has already been done. Make sure your grammar, spelling and punctuation are correct and effective so that you are conveying the meaning you intend. Statements that are vague, imprecise or ambiguous will often confuse and mislead readers, and a verbose style will add little more than padding while wasting valuable words that might be put to far better use in clear and logical explanations. Some specialised terminology may be required when reporting findings, but anything potentially unclear or confusing that has not already been defined earlier in the paper should be clarified for readers, and the same principle applies to unusual or nonstandard abbreviations. Your readers will want to understand what you are reporting about your results, not waste time looking up terms simply to understand what you are saying. A logical approach to organising your findings section (Step 2) will help you tell a logical story about your research results as you explain, highlight, offer analysis and summarise the information necessary for readers to understand the discussion section that follows.

Step 5 : Review the draft of your findings section and edit and revise until it reports your key findings exactly as you would have them presented to your readers. Check for accuracy and consistency in data across the section as a whole and all its visual elements. Read your prose aloud to catch language errors, awkward phrases and abrupt transitions. Ensure that the order in which you have presented results is the best order for focussing readers on your research objectives and preparing them for the interpretations, speculations, recommendations and other elements of the discussion that you are planning. This will involve looking back over the paper’s introductory and background material as well as anticipating the discussion and conclusion sections, and this is precisely the right point in the process for reviewing and reflecting. Your research results have taken considerable time to obtain and analyse, so a little more time to stand back and take in the wider view from the research door you have opened is a wise investment. The opinions of any additional readers you can recruit, whether they are professional mentors and colleagues or family and friends, will often prove invaluable as well.

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How To Write the Findings Section of a Research Paper These five steps will help you write a clear & interesting findings section for a research paper

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How to Write an Effective Results Section

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• 1 Rothman Orthopaedics Institute, Philadelphia, PA.
• PMID: 31145152
• DOI: 10.1097/BSD.0000000000000845

Developing a well-written research paper is an important step in completing a scientific study. This paper is where the principle investigator and co-authors report the purpose, methods, findings, and conclusions of the study. A key element of writing a research paper is to clearly and objectively report the study's findings in the Results section. The Results section is where the authors inform the readers about the findings from the statistical analysis of the data collected to operationalize the study hypothesis, optimally adding novel information to the collective knowledge on the subject matter. By utilizing clear, concise, and well-organized writing techniques and visual aids in the reporting of the data, the author is able to construct a case for the research question at hand even without interpreting the data.

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The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

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How to Write the Dissertation Findings or Results – Steps & Tips

Published by Grace Graffin at August 11th, 2021 , Revised On October 9, 2023

Each  part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .

Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.

The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.

This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.

It is vitally important to focus only on clear and precise observations. The findings chapter of the  dissertation  is theoretically the easiest to write.

It includes  statistical analysis and a brief write-up about whether or not the results emerging from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.

This article will provide detailed information about  how to   write the findings of a dissertation .

When to Write Dissertation Findings Chapter

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis  or  research questions set out in  the introduction chapter of the dissertation .

You will be required to separately report your study’s findings before moving on to the discussion chapter  if your dissertation is based on the  collection of primary data  or experimental work.

However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.

• Always report the findings of your research in the past tense.
• The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
• Avoid reporting results that are not relevant to your research questions or research hypothesis.

Does your Dissertation Have the Following?

• Great Research/Sources
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• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of the Dissertation strong.

1. Reporting Quantitative Findings

The best way to present your quantitative findings is to structure them around the research  hypothesis or  questions you intend to address as part of your dissertation project.

Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.

While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion  and  conclusion  chapters.

Any findings not directly relevant to your research questions or explanations concerning the data collection process  should be added to the dissertation paper’s appendix section.

Use of Figures and Tables in Dissertation Findings

Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.

Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.

The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.

It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.

How to Report Quantitative Findings

Here is an example of how to report quantitative results in your dissertation findings chapter;

Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

Paired Samples

Paired samples correlation, paired samples test.

Also Read: How to Write the Abstract for the Dissertation.

2. Reporting Qualitative Findings

A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.

The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.

In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.

Additional information not directly relevant to your research can be included in the appendix .

How to Report Qualitative Findings

Here is an example of how to report qualitative results in your dissertation findings chapter;

How do I report quantitative findings?

The best way to present your quantitative findings is to structure them around the  research hypothesis  or  research questions  you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.

How do I report qualitative findings?

The best way to present the  qualitative research  results is to frame your findings around the most important areas or themes that you obtained after examining the data.

An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your  research question  or  hypothesis  should be clearly mentioned for the readers.

Can I use interpretive phrases like ‘it confirms’ in the finding chapter?

No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the  discussion chapter , where you will be expected to provide your interpretation of the results in detail.

Can I report the results from other research papers in my findings chapter?

NO, you must not be presenting results from other research studies in your findings.

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Ai, ethics & human agency, collaboration, information literacy, writing process, synthesizing your research findings.

• CC BY-NC-ND 4.0 by Christine Photinos - National University, San Diego

Synthesis is something you already do in your everyday life.  For example, if you are shopping for a new car, the research question you are trying to answer is, “Which car should I buy”?  You explore available models, prices, options, and consumer reviews, and you make comparisons.  For example:  Car X costs more than car Y but gets better mileage.  Or:  Reviewers A, B, and C all prefer Car X, but their praise is based primarily on design features that aren’t important to you.  It is this analysis across sources that moves you towards an answer to your question.

Early in an academic research project you are likely to find yourself making initial comparisons—for example, you may notice that Source A arrives at a conclusion very different from that of Source B—but the task of synthesis will become central to your work when you begin drafting your research paper or presentation. 

Remember, when you synthesize, you are not just compiling information.  You are organizing that information around a specific argument or question, and this work—your own intellectual work—is central to research writing.

Below are some questions that highlight ways in which the act of synthesizing brings together ideas and generates new knowledge. 

How do the sources speak to your specific argument or research question?

Your argument or research question is the main unifying element in your project.  Keep this in the forefront of your mind when you write about your sources.  Explain how, specifically, each source supports your central claim/s or suggests possible answers to your question.  For example:  Does the source provide essential background information or a definitional foundation for your argument or inquiry? Does it present numerical data that supports one of your points or helps you answer a question you have posed?  Does it present a theory that might be applied to some aspect of your project?  Does it present a recognized expert’s insights on your topic? 

How do the sources speak to each other? 

Sometimes you will find explicit dialogue between sources (for example, Source A refutes Source B by name), and sometimes you will need to bring your sources into dialogue (for example, Source A does not mention Source B, but you observe that the two are advancing similar or dissimilar arguments).  Attending to interrelationships among sources is at the heart of the task of synthesis.

Begin by asking:  What are the points of agreement?  Where are there disagreements?

But be aware that you are unlikely to find your sources in pure positions of “for” vs. “against.”  You are more likely to find agreement in some areas and disagreement in other areas.  You may also find agreement but for different reasons—such as different underlying values and priorities, or different methods of inquiry.

(See also Identifying a Conversation )

Where are there, or aren’t there, information gaps?

Where is the available information unreliable (for example, it might be difficult to trace back to primary sources), or limited, (for example, based on just a few case studies, or on just one geographical area), or difficult for non-specialists to access (for example, written in specialist language, or tucked away in a physical archive)? 

Does your inquiry contain sub-questions that may not at present be answerable, or that may not be answerable without additional primary research—for example, laboratory studies, direct observation, interviews with witnesses or participants, etc.?

Or, alternatively, is there a great deal of reliable, accessible information that addresses your question or speaks to your argument or inquiry? 

In considering these questions, you are engaged in synthesis: you are conducting an overview assessment of the field of available information and in this way generating composite knowledge.

Remember, synthesis is about pulling together information from a range of sources in order to answer a question or construct an argument. It is something you will be called upon to do in a wide variety of academic, professional, and personal contexts. Being able to dive into an ocean of information and surface with meaningful conclusions is an essential life skill.

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Organizing Your Social Sciences Research Paper

• 8. The Discussion
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The purpose of the discussion section is to interpret and describe the significance of your findings in relation to what was already known about the research problem being investigated and to explain any new understanding or insights that emerged as a result of your research. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but the discussion does not simply repeat or rearrange the first parts of your paper; the discussion clearly explains how your study advanced the reader's understanding of the research problem from where you left them at the end of your review of prior research.

Annesley, Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Peacock, Matthew. “Communicative Moves in the Discussion Section of Research Articles.” System 30 (December 2002): 479-497.

Importance of a Good Discussion

The discussion section is often considered the most important part of your research paper because it:

• Most effectively demonstrates your ability as a researcher to think critically about an issue, to develop creative solutions to problems based upon a logical synthesis of the findings, and to formulate a deeper, more profound understanding of the research problem under investigation;
• Presents the underlying meaning of your research, notes possible implications in other areas of study, and explores possible improvements that can be made in order to further develop the concerns of your research;
• Highlights the importance of your study and how it can contribute to understanding the research problem within the field of study;
• Presents how the findings from your study revealed and helped fill gaps in the literature that had not been previously exposed or adequately described; and,
• Engages the reader in thinking critically about issues based on an evidence-based interpretation of findings; it is not governed strictly by objective reporting of information.

Annesley Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Bitchener, John and Helen Basturkmen. “Perceptions of the Difficulties of Postgraduate L2 Thesis Students Writing the Discussion Section.” Journal of English for Academic Purposes 5 (January 2006): 4-18; Kretchmer, Paul. Fourteen Steps to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008.

Structure and Writing Style

I.  General Rules

These are the general rules you should adopt when composing your discussion of the results :

• Do not be verbose or repetitive; be concise and make your points clearly
• Avoid the use of jargon or undefined technical language
• Follow a logical stream of thought; in general, interpret and discuss the significance of your findings in the same sequence you described them in your results section [a notable exception is to begin by highlighting an unexpected result or a finding that can grab the reader's attention]
• Use the present verb tense, especially for established facts; however, refer to specific works or prior studies in the past tense
• If needed, use subheadings to help organize your discussion or to categorize your interpretations into themes

II.  The Content

The content of the discussion section of your paper most often includes :

• Explanation of results : Comment on whether or not the results were expected for each set of findings; go into greater depth to explain findings that were unexpected or especially profound. If appropriate, note any unusual or unanticipated patterns or trends that emerged from your results and explain their meaning in relation to the research problem.
• References to previous research : Either compare your results with the findings from other studies or use the studies to support a claim. This can include re-visiting key sources already cited in your literature review section, or, save them to cite later in the discussion section if they are more important to compare with your results instead of being a part of the general literature review of prior research used to provide context and background information. Note that you can make this decision to highlight specific studies after you have begun writing the discussion section.
• Deduction : A claim for how the results can be applied more generally. For example, describing lessons learned, proposing recommendations that can help improve a situation, or highlighting best practices.
• Hypothesis : A more general claim or possible conclusion arising from the results [which may be proved or disproved in subsequent research]. This can be framed as new research questions that emerged as a consequence of your analysis.

III.  Organization and Structure

Keep the following sequential points in mind as you organize and write the discussion section of your paper:

• Think of your discussion as an inverted pyramid. Organize the discussion from the general to the specific, linking your findings to the literature, then to theory, then to practice [if appropriate].
• Use the same key terms, narrative style, and verb tense [present] that you used when describing the research problem in your introduction.
• Begin by briefly re-stating the research problem you were investigating and answer all of the research questions underpinning the problem that you posed in the introduction.
• Describe the patterns, principles, and relationships shown by each major findings and place them in proper perspective. The sequence of this information is important; first state the answer, then the relevant results, then cite the work of others. If appropriate, refer the reader to a figure or table to help enhance the interpretation of the data [either within the text or as an appendix].
• Regardless of where it's mentioned, a good discussion section includes analysis of any unexpected findings. This part of the discussion should begin with a description of the unanticipated finding, followed by a brief interpretation as to why you believe it appeared and, if necessary, its possible significance in relation to the overall study. If more than one unexpected finding emerged during the study, describe each of them in the order they appeared as you gathered or analyzed the data. As noted, the exception to discussing findings in the same order you described them in the results section would be to begin by highlighting the implications of a particularly unexpected or significant finding that emerged from the study, followed by a discussion of the remaining findings.
• Before concluding the discussion, identify potential limitations and weaknesses if you do not plan to do so in the conclusion of the paper. Comment on their relative importance in relation to your overall interpretation of the results and, if necessary, note how they may affect the validity of your findings. Avoid using an apologetic tone; however, be honest and self-critical [e.g., in retrospect, had you included a particular question in a survey instrument, additional data could have been revealed].
• The discussion section should end with a concise summary of the principal implications of the findings regardless of their significance. Give a brief explanation about why you believe the findings and conclusions of your study are important and how they support broader knowledge or understanding of the research problem. This can be followed by any recommendations for further research. However, do not offer recommendations which could have been easily addressed within the study. This would demonstrate to the reader that you have inadequately examined and interpreted the data.

IV.  Overall Objectives

The objectives of your discussion section should include the following: I.  Reiterate the Research Problem/State the Major Findings

Briefly reiterate the research problem or problems you are investigating and the methods you used to investigate them, then move quickly to describe the major findings of the study. You should write a direct, declarative, and succinct proclamation of the study results, usually in one paragraph.

II.  Explain the Meaning of the Findings and Why They are Important

No one has thought as long and hard about your study as you have. Systematically explain the underlying meaning of your findings and state why you believe they are significant. After reading the discussion section, you want the reader to think critically about the results and why they are important. You don’t want to force the reader to go through the paper multiple times to figure out what it all means. If applicable, begin this part of the section by repeating what you consider to be your most significant or unanticipated finding first, then systematically review each finding. Otherwise, follow the general order you reported the findings presented in the results section.

III.  Relate the Findings to Similar Studies

No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your results to those found in other studies, particularly if questions raised from prior studies served as the motivation for your research. This is important because comparing and contrasting the findings of other studies helps to support the overall importance of your results and it highlights how and in what ways your study differs from other research about the topic. Note that any significant or unanticipated finding is often because there was no prior research to indicate the finding could occur. If there is prior research to indicate this, you need to explain why it was significant or unanticipated. IV.  Consider Alternative Explanations of the Findings

It is important to remember that the purpose of research in the social sciences is to discover and not to prove . When writing the discussion section, you should carefully consider all possible explanations for the study results, rather than just those that fit your hypothesis or prior assumptions and biases. This is especially important when describing the discovery of significant or unanticipated findings.

V.  Acknowledge the Study’s Limitations

It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor! Note any unanswered questions or issues your study could not address and describe the generalizability of your results to other situations. If a limitation is applicable to the method chosen to gather information, then describe in detail the problems you encountered and why. VI.  Make Suggestions for Further Research

You may choose to conclude the discussion section by making suggestions for further research [as opposed to offering suggestions in the conclusion of your paper]. Although your study can offer important insights about the research problem, this is where you can address other questions related to the problem that remain unanswered or highlight hidden issues that were revealed as a result of conducting your research. You should frame your suggestions by linking the need for further research to the limitations of your study [e.g., in future studies, the survey instrument should include more questions that ask..."] or linking to critical issues revealed from the data that were not considered initially in your research.

NOTE: Besides the literature review section, the preponderance of references to sources is usually found in the discussion section . A few historical references may be helpful for perspective, but most of the references should be relatively recent and included to aid in the interpretation of your results, to support the significance of a finding, and/or to place a finding within a particular context. If a study that you cited does not support your findings, don't ignore it--clearly explain why your research findings differ from theirs.

V.  Problems to Avoid

• Do not waste time restating your results . Should you need to remind the reader of a finding to be discussed, use "bridge sentences" that relate the result to the interpretation. An example would be: “In the case of determining available housing to single women with children in rural areas of Texas, the findings suggest that access to good schools is important...," then move on to further explaining this finding and its implications.
• As noted, recommendations for further research can be included in either the discussion or conclusion of your paper, but do not repeat your recommendations in the both sections. Think about the overall narrative flow of your paper to determine where best to locate this information. However, if your findings raise a lot of new questions or issues, consider including suggestions for further research in the discussion section.
• Do not introduce new results in the discussion section. Be wary of mistaking the reiteration of a specific finding for an interpretation because it may confuse the reader. The description of findings [results section] and the interpretation of their significance [discussion section] should be distinct parts of your paper. If you choose to combine the results section and the discussion section into a single narrative, you must be clear in how you report the information discovered and your own interpretation of each finding. This approach is not recommended if you lack experience writing college-level research papers.
• Use of the first person pronoun is generally acceptable. Using first person singular pronouns can help emphasize a point or illustrate a contrasting finding. However, keep in mind that too much use of the first person can actually distract the reader from the main points [i.e., I know you're telling me this--just tell me!].

Analyzing vs. Summarizing. Department of English Writing Guide. George Mason University; Discussion. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Hess, Dean R. "How to Write an Effective Discussion." Respiratory Care 49 (October 2004); Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008; The Lab Report. University College Writing Centre. University of Toronto; Sauaia, A. et al. "The Anatomy of an Article: The Discussion Section: "How Does the Article I Read Today Change What I Will Recommend to my Patients Tomorrow?” The Journal of Trauma and Acute Care Surgery 74 (June 2013): 1599-1602; Research Limitations & Future Research . Lund Research Ltd., 2012; Summary: Using it Wisely. The Writing Center. University of North Carolina; Schafer, Mickey S. Writing the Discussion. Writing in Psychology course syllabus. University of Florida; Yellin, Linda L. A Sociology Writer's Guide . Boston, MA: Allyn and Bacon, 2009.

Writing Tip

Don’t Over-Interpret the Results!

Interpretation is a subjective exercise. As such, you should always approach the selection and interpretation of your findings introspectively and to think critically about the possibility of judgmental biases unintentionally entering into discussions about the significance of your work. With this in mind, be careful that you do not read more into the findings than can be supported by the evidence you have gathered. Remember that the data are the data: nothing more, nothing less.

MacCoun, Robert J. "Biases in the Interpretation and Use of Research Results." Annual Review of Psychology 49 (February 1998): 259-287; Ward, Paulet al, editors. The Oxford Handbook of Expertise . Oxford, UK: Oxford University Press, 2018.

Another Writing Tip

Don't Write Two Results Sections!

One of the most common mistakes that you can make when discussing the results of your study is to present a superficial interpretation of the findings that more or less re-states the results section of your paper. Obviously, you must refer to your results when discussing them, but focus on the interpretation of those results and their significance in relation to the research problem, not the data itself.

Azar, Beth. "Discussing Your Findings."  American Psychological Association gradPSYCH Magazine (January 2006).

Yet Another Writing Tip

Avoid Unwarranted Speculation!

The discussion section should remain focused on the findings of your study. For example, if the purpose of your research was to measure the impact of foreign aid on increasing access to education among disadvantaged children in Bangladesh, it would not be appropriate to speculate about how your findings might apply to populations in other countries without drawing from existing studies to support your claim or if analysis of other countries was not a part of your original research design. If you feel compelled to speculate, do so in the form of describing possible implications or explaining possible impacts. Be certain that you clearly identify your comments as speculation or as a suggestion for where further research is needed. Sometimes your professor will encourage you to expand your discussion of the results in this way, while others don’t care what your opinion is beyond your effort to interpret the data in relation to the research problem.

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• Stopping the gun violence epidemic

Michigan Public Health has marshaled its resources to respond to this public health crisis

May 16, 2024

By Claudia Capos

Last year, April Zeoli , associate professor of Health Management and Policy at the University of Michigan School of Public Health , traveled to Royal Oak, Michigan, to watch a much-anticipated bill-signing event take place on the steps of the district courthouse.

Prominent legislators, law enforcement officers and public safety advocates gathered in the city on May 22, 2023, to witness this historic moment in the state’s campaign against gun violence, which claimed 1,500 lives in Michigan in 2021, according to the US Centers for Disease Control and Prevention .

At the event, Michigan Gov. Gretchen Whitmer signed a landmark package of bills that included the establishment of extreme risk protection orders (ERPOs) in Michigan.

ERPOs, also known as “red flag” laws, authorize family members, police officers and healthcare professionals to seek a court order to temporarily remove the guns of someone who represents a danger to themselves or others and prevent them from purchasing guns. Michigan became the 21st state to adopt this type of legal safeguard.

“It was an important moment that will affect a lot of Michigan families in a positive way,” said Zeoli, who, like many Americans, has lost loved ones to firearms. “This is about saving lives and sparing families and friends the grief of losing someone to gun suicide or homicide.”

In recent years, public concerns about the alarming increase in firearm deaths, injuries and suicides have intensified, as news about mass shootings at schools, universities, churches, synagogues, music festivals and bars has filled social media channels and the airwaves. The CDC reports that 20,958 homicides and 26,328 suicides resulted from firearms in 2021, which is the most recent year that official data are available.

Our role at the school is to do rigorous, high-quality research that informs issues and problems. We bring science to the conversation by sharing our research findings so policymakers can be fully informed and use that information to craft their responses to an issue.”

— April Zeoli

Michigan Public Health has marshaled its resources to respond to this public health crisis. Faculty with diverse research interests are studying the causes of gun violence, exploring various interventions to address the problem, and helping to shape public policies and programs designed to make Michigan schools and communities safer.

“Our role at the school is to do rigorous, high-quality research that informs issues and problems,” said Zeoli, who is also Policy Core director for the University of Michigan Institute for Firearm Injury Prevention . “We bring science to the conversation by sharing our research findings so policymakers can be fully informed and use that information to craft their responses to an issue.”

The bill signing in May 2023 was a milestone for Zeoli, who has studied the enactment, implementation and effectiveness of ERPOs in other states and testified on firearm injury prevention policy before the US House of Representatives Gun Violence Prevention Task Force.

She brought this extensive research knowledge and expertise in policy-making to the table while Michigan legislators were drafting and evaluating the ERPO act.

“I was one of many people who weighed in on early versions of the bill with suggestions on how to improve it, based on my research and knowledge of ERPO laws in other states,” Zeoli said. “These suggestions helped legislators craft a better bill and were largely incorporated in the final version that passed the Michigan House and Senate.”

Michigan’s new ERPO law is one of four key pieces of legislation signed into law last year to reduce suicides and to keep guns out of the hands of domestic abusers, mentally unstable individuals and violent criminals. The other three laws are:

• Firearm storage laws requiring the safe storage of guns in locked containers or secured with a locking device when minors are present on the premises
• Expanded background checks related to all firearm purchases, from handguns to long guns
• A fourth law passed after the prior three will prohibit individuals convicted of misdemeanor domestic violence from accessing firearms for eight years after completion of penalties

RELATED CONTENT:

• Read more: Michigan latest state to enforce new firearm laws meant to lessen injuries, deaths
• Watch on Instagram: Brandon Wolf, survivor of the 2016 shooting at Pulse nightclub in Orlando: 'It breaks my heart that my story is not unique.'

“We have enacted common sense gun violence prevention laws supported by a majority of Michiganders including universal background checks, safe storage requirements, and extreme risk protection orders,” Whitmer said. “We got it done thanks to our state legislators, public safety officials and scholars like Dr. April Zeoli, whose research on ERPO laws will help us save lives. Let’s keep working together so people can go to work, drop their kids off at school and take a walk in their neighborhood safely.”

The new Michigan gun laws, which went into effect Feb. 13, represent an important first step in the battle to reduce firearm deaths and injuries. However, more work needs to be done to stop the epidemic of gun violence.

Vetting new firearm restrictions

ERPO laws are relatively new tools for removing firearms from people who are in danger of harming themselves or other individuals. Before 2014, only two states had these “red flag” laws and not much was known about them.

To learn more, Zeoli conducted the largest ERPO study in the United States, which entailed looking at more than 6,500 cases in six states. Her preliminary findings are quite promising.

“My research suggests that ERPOs are associated with reduced risk of suicide,” Zeoli said. “We don’t have research on homicides yet, but there’s every reason to hypothesize that ERPOs will be effective in reducing the risk of homicide as well.”

Another facet of Zeoli’s research looks at the impact of gun violence prevention laws on intimate partner homicide, where a person is killed by a current or former spouse or partner. In Michigan, a personal protection order (PPO) shields an individual from an abuser and may include a firearm restriction.

My research suggests that ERPOs are associated with reduced risk of suicide. We don’t have research on homicides yet, but there’s every reason to hypothesize that ERPOs will be effective in reducing the risk of homicide as well.”

Zeoli currently is collecting data and interviewing victims of intimate partner violence for a three-state study focused on PPOs to examine whether having a firearm restriction increases safety.

A firearm purchaser license law, such as Michigan’s, helps to ensure that abusers who are under personal or domestic violence protective orders will not be able to buy a gun when they apply for a purchase license after a background check is conducted, Zeoli said.

Her next step will be to evaluate Michigan’s new ERPO law to make sure it is working as intended.

“We will conduct research to see if the ERPO law is actually reducing firearm deaths in Michigan and what we can do to make the law more effective,” she said. “Other state legislatures will look to our findings and apply them in their own states.”

Making Michigan public schools safer

When news of the mass shooting at Oxford High School broke on Nov. 30, 2021, Justin Heinze , associate professor of Health Behavior and Health Education , sprang into action.

“The first thing I did when I heard about this shooting was to begin our outreach to area schools in Oakland County,” said Heinze, co-director of the National Center for School Safety , which recently was awarded a $7.9 million, three-year grant by the Bureau of Justice Assistance to provide expert-led training, technical assistance and evidence-based resources that address school safety challenges.

In addition, he is the director of the University of Michigan Institute for Firearm Injury Prevention’s School Safety Section. Heinze also leads Public Health IDEAS for Preventing Firearm Injuries , a Michigan Public Health initiative launched in 2021 that pursues root causes and actionable solutions to minimize firearm injuries and deaths.

I feel strongly that by using data-driven, evidence-based approaches, we will ultimately turn the tide on this disturbing trend in gun violence, just as we did with other concerns in schools, such as bullying.” 

— Justin Heinze

“It’s very difficult for schools that are going through a crisis like this, given the amount of attention, information and requests being made,” he said. “This can be overwhelming for first responders, school officials and parents. The Oxford shooting was traumatizing not just for the students and teachers, but also for other schools, the surrounding community and the entire state.”

“Gun violence takes many forms, and the causes can vary quite a bit. Therefore, prevention measures that work in one community might not be as effective in another.”

Michigan Public Health is an excellent nexus for tackling complex issues such as school shootings because it takes an interdisciplinary, multifaceted approach to problem solving, Heinze said.

“We’re applying public health principles to look at population-level interventions, community and individual engagement, and structural factors,” said Heinze, who directs the Training and Technical Assistance Center at the Institute for Firearm Injury Prevention. “I am using my behavioral theories and relying on my colleagues from medicine, social work, public policy and other disciplines to help us think about this problem.”

In June 2023, the state of Michigan awarded the institute $1 million to launch the new Michigan School Safety Initiative to improve school safety and reduce gun violence in nearly 900 Michigan public schools, enrolling approximately 1.4 million students.

Initial plans call for a three-pronged approach to enhancing school security:

• Comprehensive needs assessments of schools
• Creation of a new school safety advisory board
• Evaluation of school-safety programs  

“I feel strongly that by using data-driven, evidence-based approaches, we will ultimately turn the tide on this disturbing trend in gun violence, just as we did with other concerns in schools, such as bullying,” Heinze said.

Saving young lives

Firearm violence is now the leading cause of death among American children and teenagers.

Between 2019 and 2021, gun deaths of youth under 18 years of age rose 50%—with fatalities increasing from 1,732 to 2,590 during that time frame—according to a Pew Research Center analysis of the CDC’s latest annual mortality statistics.

But firearms are just the “tip of the iceberg,” said Marc Zimmerman , the Marshall H. Becker Collegiate Professor of Public Health. “Lots of precursors to violent behavior in general are similar to those of firearm violence.”

His research focuses on two areas: violence prevention in communities through greening work and “busy streets” initiatives and violence prevention in schools through programs for school children such as Youth Empowerment Solutions (YES).

“We wanted children to be part of the solution rather than the focus of the problem, so we developed opportunities for kids to prevent youth violence and make positive changes in their communities,” said Zimmerman, who is co-director of the Institute for Firearm Injury Prevention.

YES was piloted from 2004 through 2008 as a 12-week, after-school program in Flint and Genesee County, Michigan, to teach middle school students leadership skills, community pride, program planning and resource mobilization. Participants completed community improvement projects, including wall murals and peace gardens, to make their neighborhoods safer and more livable.

“We found that kids who went through the YES program reported less involvement in violent, aggressive or delinquent behavior and greater engagement in school and extracurricular activities compared to kids in the usual after-school programs,” Zimmerman said.

“We’ve spent billions of dollars in this nation to make roadways and cars safer and to improve policies and training programs. We were able to change people’s behavior about wearing seat belts. That saved millions of lives. And we did that without taking cars away from people or challenging their freedom to drive. I believe we can accomplish the same thing around guns, if we have the interest and commitment to do so.” 

— Marc Zimmerman

The YES curriculum has been disseminated nationwide and incorporated as a component of other community-level violence prevention strategies in other cities. Zimmerman and his colleagues have recently completed a self-paced training resource, available online, for teachers and other youth workers to learn about and implement the program in their school or community.

In addition, Zimmerman found that the areas around the youths’ community improvement projects, such as murals and community gardens, had fewer police incidents after those projects were completed, compared to the year before. This finding led researchers to work with local community groups in Flint to evaluate their efforts to improve their community by cleaning up and repurposing vacant lots.

Research has indicated that involving residents in greening neighborhoods and creating busy streets with a lot of activity not only helps people feel better about their community, but also reduces violent crimes and gun deaths.

The bottom line is that there is no one magic solution for stopping gun deaths and suicides, Zimmerman said. Solving this complex, multifaceted problem will require research, training and resources across multiple inflection points and levels of intervention.

“We’ve spent billions of dollars in this nation to make roadways and cars safer and to improve policies and training programs,” Zimmerman said. “We were able to change people’s behavior about wearing seat belts. That saved millions of lives. And we did that without taking cars away from people or challenging their freedom to drive.

“I believe we can accomplish the same thing around guns, if we have the interest and commitment to do so. But we have to listen to people with various perspectives, including those we may not agree with, to find solutions and reduce firearm-related death and injury.” 

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Rutgers Cancer Institute and RWJBarnabas Health Set to Unveil Extensive, New Cancer Research Findings at 2024 ASCO Annual Meeting

PR Newswire

NEW BRUNSWICK, N.J., May 23, 2024

49 scheduled presentations will explore several types of cancer, as well as the next frontier of health equity

NEW BRUNSWICK, N.J., May 23, 2024 /PRNewswire/ -- Clinicians and scientists from Rutgers Cancer Institute and RWJBarnabas Health will lead sessions and present their latest discoveries from their innovative cancer research program at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting, to be held in Chicago (and online) from May 30-June 4. A total of 49 accepted abstracts and presentations will cover cutting-edge topics, including two oral sessions highlighting the National Surgical Quality Improvement Program (NSQIP) audit of enhanced recovery after surgery protocols for radical cystectomy, as well as social vulnerability and clinical trial enrollment's role in the next frontier of health equity.

"Our world-renowned integrated network of researchers and clinicians at Rutgers Cancer Institute and RWJBarnabas Health continues to innovate and investigate strategies that will achieve the best possible outcomes for our patients. This is reflected in the dynamic lineup of presentations featured at the 2024 ASCO Annual Meeting, underscoring our team's commitment and dedication," said Steven K. Libutti, MD, FACS , Director, Rutgers Cancer Institute and Senior Vice President, Oncology Services, RWJBarnabas Health. "As New Jersey's only National Cancer Institute-designated Comprehensive Cancer Center and the leading cancer program in the state, we are at the forefront of advancing cancer research and care to conquer a disease that impacts so many. We look forward to sharing our array of recent advancements and findings at this year's meeting."

The research accepted for presentation at ASCO includes one late-breaking abstract, oral and poster sessions as well as publication-only abstracts highlighting data in numerous types of cancer, including breast, colorectal, lymphoma, and lung.

Highlights of the accepted abstracts include the following:

• Findings from a study that assesses how social vulnerability impacts clinical trial enrollment and explores the interaction between race and social vulnerability among patients with one of the top five cancers - breast, prostate, lung, colorectal and pancreas. Findings confirm that neighborhood social vulnerability is a barrier to trial enrollment, even more so among Black patients.
• Utilization of enhanced recovery after surgery (ERAS) protocols for radical cystectomy has been associated with improved postoperative recovery and shorter hospital stays. This study was designed to assess the impact of increasing compliance to ERAS components on postoperative outcomes in patients who underwent radical cystectomy. Researchers reviewed 3,708 patients from the National Surgical Quality Improvement Program database who underwent radical cystectomy from 2019 – 2021.
• Updates from CTEP 10492, a Phase 1/1b study investigating the AKT inhibitor ipatasertib with chemoradiation to treat locally advanced head and neck squamous cell carcinoma (HNSCC). The primary objective of this study is to determine the maximum tolerated dose and recommended Phase 2 dose of ipatasertib in combination with definitive chemoradiation therapy (CRT) in locally advanced HNSCC based on dose-limiting toxicities. This phase 1/1b study will be the first to establish safety and preliminary efficacy of ipatasertib combined with standard of care definitive CRT for HNSCC.
• Data from a Phase 3 clinical trial evaluates the efficacy and safety of odronextamab plus CHOP vs rituximab plus CHOP in previously untreated diffuse large B-cell lymphoma (DLBCL) patients. OLYMPIA-3 is a Phase 3, randomized, open-label, multicenter study of O-CHOP vs. R-CHOP in patients with previously untreated DLBCL and intermediate- or high-risk features. The primary endpoints of the study are the incidence of dose-limiting toxicities, and incidence and severity of treatment-emergent adverse events as well as progression-free survival by independent central review.
• CIPHER (NCT05333874), a single institution pilot study, evaluated whether trend of circulating tumor DNA (ctDNA) testing during neoadjuvant therapy (NAT) can serve as an early indicator of treatment response and inform disease management in the adjuvant setting. The study included 35 patients with stage II-III triple negative and HER2+ breast cancer and longitudinal ctDNA testing performed during standard of care NAT.

The full list of presentations at this year's 2024 American Society of Clinical Oncology Annual Meeting can be found here .

About Rutgers Cancer Institute As New Jersey's only National Cancer Institute-designated Comprehensive Cancer Center, Rutgers Cancer Institute, together with RWJBarnabas Health, offers the most advanced cancer treatment options, including bone marrow transplantation, proton therapy, CAR T-cell therapy and complex surgical procedures. Along with clinical trials and novel therapeutics such as precision medicine and immunotherapy – many of which are not widely available – patients have access to these cutting-edge therapies at Rutgers Cancer Institute in New Brunswick, Rutgers Cancer Institute at University Hospital in Newark, as well as through RWJBarnabas Health facilities.

For journalists – contact: Krista Didzbalis  Corporate Communications Specialist, Strategic Communications, RWJBarnabas Health 732.507.8307 [email protected]

For patient appointments/inquiries – contact: 844-CANCERNJ (844-226-2376)

SOURCE Rutgers Cancer Institute and RWJBarnabas Health

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NASA’s TESS Finds Intriguing World Sized Between Earth, Venus

Using observations by NASA’s TESS (Transiting Exoplanet Survey Satellite) and many other facilities, two international teams of astronomers have discovered a planet between the sizes of Earth and Venus only 40 light-years away. Multiple factors make it a candidate well-suited for further study using NASA’s James Webb Space Telescope .

TESS stares at a large swath of the sky for about a month at a time, tracking the brightness changes of tens of thousands of stars at intervals ranging from 20 seconds to 30 minutes. Capturing transits — brief, regular dimmings of stars caused by the passage of orbiting worlds — is one of the mission’s primary goals.

“We’ve found the nearest, transiting, temperate, Earth-size world located to date,” said Masayuki Kuzuhara, a project assistant professor at the Astrobiology Center in Tokyo, who co-led one research team with Akihiko Fukui, a project assistant professor at the University of Tokyo . “Although we don’t yet know whether it possesses an atmosphere, we’ve been thinking of it as an exo-Venus, with similar size and energy received from its star as our planetary neighbor in the solar system.”

The host star, called Gliese 12, is a cool red dwarf located almost 40 light-years away in the constellation Pisces. The star is only about 27% of the Sun’s size, with about 60% of the Sun’s surface temperature. The newly discovered world, named Gliese 12 b, orbits every 12.8 days and is Earth’s size or slightly smaller — comparable to Venus. Assuming it has no atmosphere, the planet has a surface temperature estimated at around 107 degrees Fahrenheit (42 degrees Celsius).

Astronomers say that the diminutive sizes and masses of red dwarf stars make them ideal for finding Earth-size planets. A smaller star means greater dimming for each transit, and a lower mass means an orbiting planet can produce a greater wobble , known as “reflex motion,” of the star. These effects make smaller planets easier to detect.

The lower luminosities of red dwarf stars also means their habitable zones — the range of orbital distances where liquid water could exist on a planet’s surface — lie closer to them. This makes it easier to detect transiting planets within habitable zones around red dwarfs than those around stars emitting more energy.

The distance separating Gliese 12 and the new planet is just 7% of the distance between Earth and the Sun. The planet receives 1.6 times more energy from its star as Earth does from the Sun and about 85% of what Venus experiences.

“Gliese 12 b represents one of the best targets to study whether Earth-size planets orbiting cool stars can retain their atmospheres, a crucial step to advance our understanding of habitability on planets across our galaxy,” said Shishir Dholakia, a doctoral student at the Centre for Astrophysics at the University of Southern Queensland in Australia. He co-led a different research team with Larissa Palethorpe, a doctoral student at the University of Edinburgh and University College London .

Both teams suggest that studying Gliese 12 b may help unlock some aspects of our own solar system’s evolution.

“It is thought that Earth’s and Venus’s first atmospheres were stripped away and then replenished by volcanic outgassing and bombardments from residual material in the solar system,” Palethorpe explained. “The Earth is habitable, but Venus is not due to its complete loss of water. Because Gliese 12 b is between Earth and Venus in temperature, its atmosphere could teach us a lot about the habitability pathways planets take as they develop.”

One important factor in retaining an atmosphere is the storminess of its star. Red dwarfs tend to be magnetically active, resulting in frequent, powerful X-ray flares. However, analyses by both teams conclude that Gliese 12 shows no signs of extreme behavior.

A paper led by Kuzuhara and Fukui was published May 23 in The Astrophysical Journal Letters . The Dholakia and Palethorpe findings were published in Monthly Notices of the Royal Astronomical Society on the same day.

During a transit, the host star’s light passes through any atmosphere. Different gas molecules absorb different colors, so the transit provides a set of chemical fingerprints that can be detected by telescopes like Webb. 

“We know of only a handful of temperate planets similar to Earth that are both close enough to us and meet other criteria needed for this kind of study, called transmission spectroscopy, using current facilities,” said Michael McElwain, a research astrophysicist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, and a co-author of the Kuzuhara and Fukui paper. “To better understand the diversity of atmospheres and evolutionary outcomes for these planets, we need more examples like Gliese 12 b.”

TESS is a NASA Astrophysics Explorer mission managed by NASA Goddard and operated by MIT in Cambridge, Massachusetts. Additional partners include Northrop Grumman, based in Falls Church, Virginia; NASA’s Ames Research Center in California’s Silicon Valley; the Center for Astrophysics | Harvard & Smithsonian in Cambridge, Massachusetts; MIT’s Lincoln Laboratory; and the Space Telescope Science Institute in Baltimore. More than a dozen universities, research institutes, and observatories worldwide are participants in the mission.

By Francis Reddy NASA’s Goddard Space Flight Center , Greenbelt, Md. Media Contact: Claire Andreoli 301-286-1940 [email protected] NASA’s Goddard Space Flight Center, Greenbelt, Md.

Related Terms

• Astrophysics
• Astrophysics Division
• Exoplanet Atmosphere
• Exoplanet Discoveries
• Exoplanet Exploration Program
• Exoplanet Science
• Exoplanet Transits
• Goddard Space Flight Center
• James Webb Space Telescope (JWST)
• Terrestrial Exoplanets
• TESS (Transiting Exoplanet Survey Satellite)
• The Universe

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Teens who use marijuana are more likely to suffer psychotic disorders, study finds

Teenagers who used cannabis within the last year had a dramatically higher rate of developing a psychotic disorder, according to a study published Wednesday. 

The study, led by researchers from the University of Toronto, found an 11 times higher risk of developing a psychotic disorder among teenagers who used cannabis compared with those who did not. When the analysis was limited to just emergency room visits and hospitalizations, there was a 27-fold increase in psychotic disorders in teenagers who had used the drug. 

“When I see youths with psychotic symptoms, they’re almost always using lots of cannabis,” said Dr. Leslie Hulvershorn, a child psychiatrist and chair of the psychiatry department at Indiana University who was not involved with the study. “It would be unusual to see someone present with psychotic symptoms to a hospital and not have smoked cannabis.”

The paper adds to the growing body of research that links cannabis to an increased risk of psychotic disorders, particularly in adolescence. Use of marijuana, particularly higher-potency products, has been linked to a variety of mental health disorders, including schizophrenia, anxiety and depression .

“I think that there’s enough evidence out there for us to give recommendations that teens probably shouldn’t be using cannabis,” said the study’s lead author, Andre McDonald, a postdoctoral research fellow at McMaster University in Hamilton, Ontario. “If we can somehow ask teens to delay their use until their brain has developed a little further, I think that would be good for public health.”

While most teenagers who use cannabis will not develop psychotic disorders, McDonald said, the findings are concerning given how debilitating these conditions can be. 

The new study, like previous research on marijuana and psychosis, does not directly prove that marijuana is causing psychotic disorders. While it’s possible that teens who were prone to develop psychotic disorders could have also been more likely to use cannabis, it’s unlikely because of how striking the association was, Hulvershorn said. 

“The magnitude of the effect here is just hard to believe that it’s not related to cannabis,” Hulvershorn said. 

There was no association between cannabis use and psychotic disorders in people ages 20 to 33. 

“There’s something about that stage of brain development that we haven’t yet fully characterized — where there’s a window of time where cannabis use may increase the risk of psychosis,” said Dr. Kevin Gray, a professor of psychiatry and director of addiction sciences at the Medical University of South Carolina who was not involved with the study. “This study really puts a fine point on delaying cannabis use until your 20s may mitigate one of the most potentially serious risks.”

The Biden administration has been moving toward rescheduling marijuana from Schedule I to the less dangerous Schedule III, which would also acknowledge its medical benefits at the federal level. While the potential change is expected later this year, cannabis is currently legal in 24 states for recreational use.

Marijuana use among high school students has remained steady in recent years. Nearly 1 in 3 12th graders reported using it in the previous year, according to the 2023 Monitoring the Future Survey, an annual survey that measures drug and alcohol use among adolescent students nationwide. 

The new research, published in the respected journal Psychological Medicine, includes data from over 11,000 teens and young adults who were ages 12 to 24 at the beginning of the study.

The authors pulled from the annual Canadian Community Health Survey, focusing on 2009 to 2012. Participants were then followed for up to nine years after the initial survey to track any visits they may have had to doctors or emergency rooms or any times they were admitted to hospitals.. 

Of the teens who were hospitalized or visited emergency rooms for psychotic disorders, roughly 5 in 6 had reported previous cannabis use.

“We see this replicated over and over again that there’s this developmental window of adolescence that’s very high-risk,” Gray said. 

It’s not completely clear why, he added, but one theory is that disruptions to the endocannabinoid system in adolescence may make psychotic symptoms more likely. The endocannabinoid system is a complex signaling system in the brain that marijuana targets. That could make it harder to distinguish reality from what is going on inside the head, leading to symptoms such as hallucinations. 

The authors did not specifically look at how the potency of marijuana products affected the risk of mental disorders, although previous research has found an increased risk .

Akshay Syal, M.D., is a medical fellow with the NBC News Health and Medical Unit. 

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• v.4(3); 2020 Jun

Communicating and disseminating research findings to study participants: Formative assessment of participant and researcher expectations and preferences

Cathy l. melvin.

1 College of Medicine, Medical University of South Carolina, Charleston, SC, USA

Jillian Harvey

2 College of Health Professions/Healthcare Leadership & Management, Medical University of South Carolina, Charleston, SC, USA

Tara Pittman

3 South Carolina Clinical & Translational Research Institute (CTSA), Medical University of South Carolina, Charleston, SC, USA

Stephanie Gentilin

Dana burshell.

4 SOGI-SES Add Health Study Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Teresa Kelechi

5 College of Nursing, Medical University of South Carolina, Charleston, SC, USA

Introduction:

Translating research findings into practice requires understanding how to meet communication and dissemination needs and preferences of intended audiences including past research participants (PSPs) who want, but seldom receive, information on research findings during or after participating in research studies. Most researchers want to let others, including PSP, know about their findings but lack knowledge about how to effectively communicate findings to a lay audience.

We designed a two-phase, mixed methods pilot study to understand experiences, expectations, concerns, preferences, and capacities of researchers and PSP in two age groups (adolescents/young adults (AYA) or older adults) and to test communication prototypes for sharing, receiving, and using information on research study findings.

Principal Results:

PSP and researchers agreed that sharing study findings should happen and that doing so could improve participant recruitment and enrollment, use of research findings to improve health and health-care delivery, and build community support for research. Some differences and similarities in communication preferences and message format were identified between PSP groups, reinforcing the best practice of customizing communication channel and messaging. Researchers wanted specific training and/or time and resources to help them prepare messages in formats to meet PSP needs and preferences but were unaware of resources to help them do so.

Conclusions:

Our findings offer insight into how to engage both PSP and researchers in the design and use of strategies to share research findings and highlight the need to develop services and support for researchers as they aim to bridge this translational barrier.

Introduction

Since 2006, the National Institutes of Health Clinical and Translational Science Awards (CTSA) have aimed to advance science and translate knowledge into evidence that, if implemented, helps patients and providers make more informed decisions with the potential to improve health care and health outcomes [ 1 , 2 ]. This aim responded to calls by leaders in the fields of comparative effectiveness research, clinical trials, research ethics, and community engagement to assure that results of clinical trials were made available to participants and suggesting that providing participants with results both positive and negative should be the “ethical norm” [ 1 , 3 ]. Others noted that

on the surface, the concept of providing clinical trial results might seem straightforward but putting such a plan into action will be much more complicated. Communication with patients following participation in a clinical trial represents an important and often overlooked aspect of the patient-physician relationship. Careful exploration of this issue, both from the patient and clinician-researcher perspective, is warranted [ 4 ].

Authors also noted that no systematic approach to operationalizing this “ethical norm” existed and that evidence was lacking to describe either positive or negative outcomes of sharing clinical trial results with study participants and the community [ 4 ]. It was generally assumed, but not supported by research, that sharing would result in better patient–physician/researcher communication, improvement in patient care and satisfaction with care, better patient/participant understanding of clinical trials, and enhanced clinical trial accrual [ 4 ].

More recent literature informs these processes but also raises unresolved concerns about the communication and dissemination of research results. A 2008 narrative review of available data on the effects of communicating aggregate and individual research showed that

• research participants want aggregate and clinically significant individual study results made available to them despite the transient distress that communication of results sometimes elicits [ 3 , 5 ]. While differing in their preferences for specific channels of communication, they indicated that not sharing results fostered lack of participant trust in the health-care system, providers, and researchers [ 6 ] and an adverse impact on trial participation [ 5 ];
• investigators recognized their ethical obligation to at least offer to share research findings with recipients and the nonacademic community but differed on whether they should proactively re-contact participants, the type of results to be offered to participants, the need for clinical relevance before disclosure, and the stage at which research results should be offered [ 5 ]. They also reported not being well versed in communication and dissemination strategies known to be effective and not having funding sources to implement proven strategies for sharing with specific audiences [ 5 ];
• members of the research enterprise noted that while public opinion regarding participation in clinical trials is positive, clinical trial accrual remains low and that the failure to provide information about study results may be one of many factors negatively affecting accrual. They also called for better understanding of physician–researcher and patient attitudes and preferences and posit that development of effective mechanisms to share trial results with study participants should enhance patient–physician communication and improve clinical care and research processes [ 5 ].

A 2010 survey of CTSAs found that while professional and scientific audiences are currently the primary focus for communicating and disseminating research findings, it is equally vital to develop approaches for sharing research findings with other audiences, including individuals who participate in clinical trials [ 1 , 5 ]. Effective communication and dissemination strategies are documented in the literature [ 6 , 7 ], but most are designed to promote adoption of evidence-based interventions and lack of applicability to participants overall, especially to participants who are members of special populations and underrepresented minorities who have fewer opportunities to participate in research and whose preferences for receiving research findings are unknown [ 7 ].

Researchers often have limited exposure to methods that offer them guidance in communicating and disseminating study findings in ways likely to improve awareness, adoption, and use of their findings [ 7 ]. Researchers also lack expertise in using communication channels such as traditional journalism platforms, live or face-to-face events such as public festivals, lectures, and panels, and online interactions [ 8 ]. Few strategies provide guidance for researchers about how to develop communications that are patient-centered, contain plain language, create awareness of the influence of findings on participant or population health, and increase the likelihood of enrollment in future studies.

Consequently, researchers often rely on traditional methods (e.g., presentations at scientific meetings and publication of study findings in peer-reviewed journals) despite evidence suggesting their limited reach and/or impact among professional/scientific and/or lay audiences [ 9 , 10 ].

Input from stakeholders can enhance our understanding of how to assure that participants will receive understandable, useful information about research findings and, as appropriate, interpret and use this information to inform their decisions about changing health behaviors, interacting with their health-care providers, enrolling in future research studies, sharing their study experiences with others, or recommending to others that they participate in studies.

Purpose and Goal

This pilot project was undertaken to address issues cited above and in response to expressed concerns of community members in our area about not receiving information on research studies in which they participated. The project design, a two-phase, mixed methods pilot study, was informed by their subsequent participation in a committee of community-academic representatives to determine possible options for improving the communication and dissemination of study results to both study participants and the community at large.

Our goals were to understand the experiences, expectations, concerns, preferences, and capacities of researchers and past research participants (PSP) in two age groups (adolescents/young adults (AYA) aged 15–25 years and older adults aged 50 years or older) and to test communication prototypes for sharing, receiving, and using information on research study findings. Our long-term objectives are to stimulate new, interdisciplinary collaborative research and to develop resources to meet PSP and researcher needs.

This study was conducted in an academic medical center located in south-eastern South Carolina. Phase one consisted of surveying PSP and researchers. In phase two, in-person focus groups were conducted among PSP completing the survey and one-on-one interviews were conducted among researchers. Participants in either the interviews or focus groups responded to a set of questions from a discussion guide developed by the study team and reviewed three prototypes for communicating and disseminating study results developed by the study team in response to PSP and researcher survey responses: a study results letter, a study results email, and a web-based communication – Mail Chimp (Figs.  1 – 3 ).

Prototype 1: study results email prototype. MUSC, Medical University of South Carolina.

Prototype 3: study results MailChimp prototypes 1 and 2. MUSC, Medical University of South Carolina.

Prototype 2: study results letter prototype.

PSP and researcher surveys

A 42-item survey questionnaire representing seven domains was developed by a multidisciplinary team of clinicians, researchers, and PSP that evaluated the questions for content, ease of understanding, usefulness, and comprehensiveness [ 11 ]. Project principal investigators reviewed questions for content and clarity [ 11 ]. The PSP and researcher surveys contained screening and demographic questions to determine participant eligibility and participant characteristics. The PSP survey assessed prior experience with research, receipt of study information from the research team, intention to participate in future research, and preferences and opinions about receipt of information about study findings and next steps. Specific questions for PSP elicited their preferences for communication channels such as phone call, email, social or mass media, and public forum and included channels unique to South Carolina, such as billboards. PSP were asked to rank their preferences and experiences regarding receipt of study results using a Likert scale with the following measurements: “not at all interested” (0), “not very interested” (1), “neutral” (3), “somewhat interested” (3), and “very interested” (4).

The researcher survey contained questions about researcher decisions, plans, and actions regarding communication and dissemination of research results for a recently completed study. Items included knowledge and opinions about how to communicate and disseminate research findings, resources used and needed to develop communication strategies, and awareness and use of dissemination channels, message development, and presentation format.

A research team member administered the survey to PSP and researchers either in person or via phone. Researchers could also complete the survey online through Research Electronic Data Capture (REDCap©).

Focus groups and discussion guide content

The PSP focus group discussion guide contained questions to assess participants’ past experiences with receiving information about research findings; identify participant preferences for receiving research findings whether negative, positive, or equivocal; gather information to improve communication of research results back to participants; assess participant intention to enroll in future research studies, to share their study experiences with others, and to refer others to our institution for study participation; and provide comments and suggestions on prototypes developed for communication and dissemination of study results. Five AYA participated in one focus group, and 11 older adults participated in one focus group. Focus groups were conducted in an off-campus location with convenient parking and at times convenient for participants. Snacks and beverages were provided.

The researcher interview guide was designed to understand researchers’ perspectives on communicating and disseminating research findings to participants; explore past experiences, if any, of researchers with communication and dissemination of research findings to study participants; document any approaches researchers may have used or intend to use to communicate and disseminate research findings to study participants; assess researcher expectations of benefits associated with sharing findings with participants, as well as, perceived and actual barriers to sharing findings; and provide comments and suggestions on prototypes developed for communication and dissemination of study results.

Prototype materials

Three prototypes were presented to focus group participants and included (1) a formal letter on hospital letterhead designed to be delivered by standard mail, describing the purpose and findings of a fictional study and thanking the individual for his/her participation, (2) a text-only email including a brief thank you and a summary of major findings with a link to a study website for more information, and (3) an email formatted like a newsletter with detailed information on study purpose, method, and findings with graphics to help convey results. A mock study website was shown and included information about study background, purpose, methods, results, as well as, links to other research and health resources. Prototypes were presented either in paper or PowerPoint format during the focus groups and explained by a study team member who then elicited participant input using the focus group guide. Researchers also reviewed and commented on prototype content and format in one-on-one interviews with a study team member.

Protection of Human Subjects

The study protocol (No. Pro00067659) was submitted to and approved by the Institutional Review Board at the Medical University of South Carolina in 2017. PSP (or the caretakers for PSP under age 18), and researchers provided verbal informed consent prior to completing the survey or participating in either a focus group or interview. Participants received a verbal introduction prior to participating in each phase.

Recruitment and Interview Procedures

Past study participants.

A study team member reviewed study participant logs from five recently completed studies at our institution involving AYA or older adults to identify individuals who provided consent for contact regarding future studies. Subsequent PSP recruitment efforts based on these searches were consistent with previous contact preferences recorded in each study participant’s consent indicating desire to be re-contacted. The primary modes of contact were phone/SMS and email.

Efforts to recruit other PSP were made through placement of flyers in frequented public locations such as coffee shops, recreation complexes, and college campuses and through social media, Yammer, and newsletters. ResearchMatch, a web-based recruitment tool, was used to alert its subscribers about the study. Potential participants reached by these methods contacted our study team to learn more about the study, and if interested and pre-screened eligible, volunteered and were consented for the study. PSP completing the survey indicated willingness to share experiences with the study team in a focus group and were re-contacted to participate in focus groups.

Researcher recruitment

Researchers were identified through informal outreach by study investigators and staff, a flyer distributed on campus, use of Yammer and other institutional social media platforms, and internal electronic newsletters. Researchers responding to these recruitment efforts were invited to participate in the researcher survey and/or interview.

Incentives for participation

Researchers and PSP received a $25 gift card for completing the survey and $75 for completing the interview (researcher) or focus group (PSP) (up to $100 per researcher or PSP).

Data tables displaying demographic and other data from the PSP surveys (Table ​ (Table1) 1 ) were prepared from the REDCap© database and responses reported as number and percent of respondents choosing each response option.

Post study participant (PSP) characteristics by Adolescents/Young Adults (AYA), Older Adults, and ALL (All participants regardless of age)

Age mean (SD) = 49.7 (18.6).

Focus group and researcher interview data were recorded (either via audio recording and/or notes taken by research staff) and analyzed via a general inductive qualitative approach, a method appropriate for program evaluation studies and aimed at condensing large amounts of textual data into frameworks that describe the underlying process and experiences under study [ 12 ]. Data were analyzed by our team’s qualitative expert who read the textual data multiple times, developed a coding scheme to identify themes in the textual data, and used group consensus methods with other team members to identify unique, key themes.

Sixty-one of sixty-five PSP who volunteered to participate in the PSP survey were screened eligible, fifty were consented, and forty-eight completed the survey questionnaire. Of the 48 PSP completing the survey, 15 (32%) were AYA and 33 (68%) older adults. The mean age of survey respondents was 49.7 years, 23.5 for AYA, and 61.6 for older adults. Survey respondents were predominantly White, non-Hispanic/Latino, female, and with some college or a college degree (Table ​ (Table1). 1 ). The percentage of participants in each group never or rarely needing any help with reading/interpreting written materials was above 93% in both groups.

Over 90% of PSP responded that they would participate in another research study, and more than 75% of PSP indicated that study participants should know about study results. Most (68.8%) respondents indicated that they did not receive any communications from study staff after they finished a study .

PSP preferences for communication channel are summarized in Table ​ Table2 2 and based on responses to the question “How do you want to receive information?.” Both AYA and older adults agree or completely agree that they prefer email to other communication channels and that billboards did not apply to them. Older adult preferences for communication channels as indicated by agreeing or completely agreeing were in ranked order of highest to lowest: use of mailed letters/postcards, newsletter, and phone. A majority (over 50%) of older adults completely disagreed or disagreed on texting and social media as options and had only slight preference for mass media, public forum, and wellness fairs or expos.

Communication preference by group: AYA * , older adult ** , and ALL ( n = 48)

ALL, total per column.

While AYA preferred email over all other options, they completely disagreed/disagreed with mailed letters/postcards, social media, and mass media options.

When communication formats were ranked overall by each group and by both groups combined, the ranking from most to least preferred was written materials, opportunities to interact with study teams and ask questions, visual charts, graphs, pictures, and videos, audios, and podcasts.

PSP Focus Groups

PSP want to receive and share information on study findings for studies in which he/she participated. Furthermore, participants stated their desire to share study results across social networks and highlighted opportunities to share communicated study results with their health-care providers, family members, friends, and other acquaintances with similar medical conditions.

Because of the things I was in a study for, it’s a condition I knew three other people who had the same condition, so as soon as it worked for me, I put the word out, this is great stuff. I would forward the email with the link, this is where you can go to also get in on this study, or I’d also tell them, you know, for me, like the medication. Here’s the medication. Here’s the name of it. Tell your doctor. I would definitely share. I’d just tell everyone without a doubt. Right when I get home, as soon as I walk in the door, and say Renee-that’s my daughter-I’ve got to tell you this.

Communication of study information could happen through several channels including social media, verbal communication, sharing of written documents, and forwarding emails containing a range of content in a range of formats (e.g., reports and pamphlets).

Word of mouth and I have no shame in saying I had head to toe psoriasis, and I used the drug being studied, and so I would just go to people, hey, look. So, if you had it in paper form, like a pamphlet or something, yeah I’d pass it on to them.

PSP prefer clear, simple messaging and highlighted multiple, preferred communication modalities for receiving information on study findings including emails, letters, newsletters, social media, and websites.

The wording is really simple, which I like. It’s to the point and clear. I really like the bullet points, because it’s quick and to the point. I think the [long] paragraphs-you get lost, especially when you are reading on your phone.

They indicated a clear preference for colorful, simple, easy to read communication. PSP also expressed some concern about difficulty opening emails with pictures and dislike lengthy written text. “I don’t read long emails. I tend to delete them”

PSP indicated some confusion about common research language. For example, one participant indicated that using the word “estimate” indicates the research findings were an approximation, “When I hear those words, I just think you’re guessing, estimate, you know? It sounds like an estimate, not a definite answer.”

Researcher Survey

Twenty-three of thirty-two researchers volunteered to participate in the researcher survey, were screened eligible, and two declined to participate, resulting in 19 who provided consent to participate and completed the survey. The mean age of survey respondents was 51.8 years. Respondents were predominantly White, non-Hispanic/Latino, and female, and all were holders of either a professional school degree or a doctoral degree. When asked if it is important to inform study participants of study results, 94.8% of responding researchers agreed that it was extremely important or important. Most researchers have disseminated findings to study participants or plan to disseminate findings.

Researchers listed a variety of reasons for their rating of the importance of informing study participants of study results including “to promote feelings of inclusion by participants and other community members”, “maintaining participant interest and engagement in the subject study and in research generally”, “allowing participants to benefit somewhat from their participation in research and especially if personal health data are collected”, “increasing transparency and opportunities for learning”, and “helping in understanding the impact of the research on the health issue under study”.

Some researchers view sharing study findings as an “ethical responsibility and/or a tenet of volunteerism for a research study”. For example, “if we (researchers) are obligated to inform participants about anything that comes up during the conduct of the study, we should feel compelled to equally give the results at the end of the study”.

One researcher “thought it a good idea to ask participants if they would like an overview of findings at the end of the study that they could share with others who would like to see the information”.

Two researchers said that sharing research results “depends on the study” and that providing “general findings to the participants” might be “sufficient for a treatment outcome study”.

Researchers indicated that despite their willingness to share study results, they face resource challenges such as a lack of funding and/or staff to support communication and dissemination activities and need assistance in developing these materials. One researcher remarked “I would really like to learn what are (sic) the best ways to share research findings. I am truly ignorant about this other than what I have casually observed. I would enjoy attending a workshop on the topic with suggested templates and communication strategies that work best” and that this survey “reminds me how important this is and it is promising that our CTSA seems to plan to take this on and help researchers with this important study element.”

Another researcher commented on a list of potential types of assistance that could be made available to assist with communicating and disseminating results, that “Training on developing lay friendly messaging is especially critically important and would translate across so many different aspects of what we do, not just dissemination of findings. But I’ve noticed that it is a skill that very few people have, and some people never can seem to develop. For that reason, I find as a principal investigator that I am spending a lot of my time working on these types of materials when I’d really prefer research assistant level folks having the ability to get me 99% of the way there.”

Most researchers indicated that they provide participants with personal tests or assessments taken from the study (60% n = 6) and final study results (72.7%, n = 8) but no other information such as recruitment and retention updates, interim updates or results, information on the impact of the study on either the health topic of the study or the community, information on other studies or provide tips and resources related to the health topic and self-help. Sixty percent ( n = 6) of researcher respondents indicated sharing planned next steps for the study team and information on how the study results would be used.

When asked about how they communicated results, phone calls were mentioned most frequently followed by newsletters, email, webpages, public forums, journal article, mailed letter or postcard, mass media, wellness fairs/expos, texting, or social media.

Researchers used a variety of communication formats to communicate with study participants. Written descriptions of study findings were most frequently reported followed by visual depictions, opportunities to interact with study staff and ask questions or provide feedback, and videos/audio/podcasts.

Seventy-three percent of researchers reported that they made efforts to make study findings information available to those with low levels of literacy, health literacy, or other possible limitations such as non-English-speaking populations.

In open-ended responses, most researchers reported wanting to increase their awareness and use of on-campus training and other resources to support communication and dissemination of study results, including how to get resources and budgets to support their use.

Researcher Interviews

One-on-one interviews with researchers identified two themes.

Researchers may struggle to see the utility of communicating small findings

Some researchers indicated hesitancy in communicating preliminary findings, findings from small studies, or highly summarized information. In addition, in comparison to research participants, researchers seemed to place a higher value on specific details of the study.

“I probably wouldn’t put it up [on social media] until the actual manuscript was out with the graphs and the figures, because I think that’s what people ultimately would be interested in.”

Researchers face resource and time limitations in communication and dissemination of study findings

Researchers expressed interest in communicating research results to study participants. However, they highlighted several challenges including difficulties in tracking current email and physical addresses for participants; compliance with literacy and visual impairment regulations; and the number of products already required in research that consume a considerable amount of a research team’s time. Researchers expressed a desire to have additional resources and templates to facilitate sharing study findings. According to one respondent, “For every grant there is (sic) 4-10 papers and 3-5 presentations, already doing 10-20 products.” Researchers do not want to “reinvent the wheel” and would like to pull from existing papers and presentations on how to share with participants and have boilerplate, writing templates, and other logistical information available for their use.

Researchers would also like training in the form of lunch-n-learns, podcasts, or easily accessible online tools on how to develop materials and approaches. Researchers are interested in understanding the “do’s and don’ts” of communicating and disseminating study findings and any regulatory requirements that should be considered when communicating with research participants following a completed study. For example, one researcher asked, “From beginning to end – the do’s and don’ts – are stamps allowed as a direct cost? or can indirect costs include paper for printing newsletters, how about designing a website, a checklist for pulling together a newsletter?”

The purpose of this pilot study was to explore the current experiences, expectations, concerns, preferences, and capacities of PSP including youth/young adult and older adult populations and researchers for sharing, receiving, and using information on research study findings. PSP and researchers agreed, as shown in earlier work [ 3 , 5 ], that sharing information upon study completion with participants was something that should be done and that had value for both PSP and researchers. As in prior studies [ 3 , 5 ], both groups also agreed that sharing study findings could improve ancillary outcomes such as participant recruitment and enrollment, use of research findings to improve health and health-care delivery, and build overall community support for research. In addition, communicating results acknowledges study participants’ contributions to research, a principle firmly rooted in respect for treating participants as not merely a means to further scientific investigation [ 5 ].

The majority of PSP indicated that they did not receive research findings from studies they participated in, that they would like to receive such information, and that they preferred specific communication methods for receipt of this information such as email and phone calls. While our sample was small, we did identify preferences for communication channels and for message format. Some differences and similarities in preferences for communication channels and message format were identified between AYA and older adults, thus reinforcing the best practice of customizing communication channel and messaging to each specific group. However, the preference for email and the similar rank ordering of messaging formats suggest that there are some overall communication preferences that may apply to most populations of PSP. It remains unclear whether participants prefer individual or aggregate results of study findings and depends on the type of study, for example, individual results of genotypes versus aggregate results of epidemiological studies [ 13 ]. A study by Miller et al suggests that the impact of receiving aggregate results, whether clinically relevant or not, may equal that of receiving individual results [ 14 ]. Further investigation warrants evaluation of whether, when, and how researchers should communicate types of results to study participants, considering multiple demographics of the populations such as age and ethnicity on preferences.

While researchers acknowledged that PSP would like to hear from them regarding research results and that they wanted to meet this expectation, they indicated needing specific training and/or time and resources to provide this information to PSP in a way that meets PSP needs and preferences. Costs associated with producing reports of findings were a concern of researchers in our study, similar to findings from a study conducted by Di Blasi and colleagues in which 15% (8 of 53 investigators) indicated that they wanted to avoid extra costs associated with the conduct of their studies and extra administrative work [ 15 ]. In this same study, the major reason for not informing participants about study results was that forty percent of investigators never considered this option. Researchers were unaware of resources available on existing platforms at their home institution or elsewhere to help them with communication and dissemination efforts [ 10 ].

Addressing Barriers to Implementation

Information from academic and other organizations on how to best communicate research findings in plain language is available and could be shared with researchers and their teams. The Cochrane Collaborative [ 16 ], the Centers for Disease Control and Prevention [ 17 ], and the Patient-Centered Outcomes Research Institute [ 18 ] have resources to help researchers develop plain language summaries using proven approaches to overcome literacy and other issues that limit participant access to study findings. Some academic institutions have electronic systems in place to confidentially share templated laboratory and other personal study information with participants and, if appropriate, with their health-care providers.

Limitations

Findings from the study are limited by several study and respondent characteristics. The sample was drawn from research records at one university engaging in research in a relatively defined geographic area and among two special populations: AYA and older adults. As such, participants were not representative of either the general population in the area, the population of PSP or researchers available in the area, or the racial and ethnic diversity of potential and/or actual participants in the geographic area. The small number of researcher participants did not represent the pool of researchers at the university, and the research studies from which participants were drawn were not representative of the broad range of clinical and translational research undertaken by our institution or within the geographic community it serves. The number of survey and focus group participants was insufficient to allow robust analysis of findings specific to participants’ race, ethnicity, gender, or membership in the target age groups of AYA or older adult. However, these data will inform a future trial with adequate representations from underrepresented and special population groups.

Since all PSP had participated in research, they may have been biased in favor of wanting to know more about study results and/or supportive/nonsupportive of the method of communication/dissemination they were exposed to through their participation in these studies.

Conclusions

Our findings provide information from PSP and researchers on their expectations about sharing study findings, preferences for how to communicate and disseminate study findings, and need for greater assistance in removing roadblocks to using proven communication and dissemination approaches. This information illustrates the potential to engage both PSP and researchers in the design and use of communication and dissemination strategies and materials to share research findings, engage in efforts to more broadly disseminate research findings, and inform our understanding of how to interpret and communicate research findings for members of special population groups. While several initial prototypes were developed in response to this feedback and shared for review by participants in this study, future research will focus on finalizing and testing specific communication and dissemination prototypes aimed at these special population groups.

Findings from our study support a major goal of the National Center for Advancing Translational Science Recruitment Innovation Center to engage and collaborate with patients and their communities to advance translation science. In response to the increased awareness of the importance of sharing results with study participants or the general public, a template for dissemination of research results is available in the Recruitment and Retention Toolbox through the CTSA Trial Innovation Network (TIN: trialinnovationnetwork.org ). We believe that our findings will inform resources for use in special populations through collaborations within the TIN.

Acknowledgment

This pilot project was supported, in part, by the National Center for Advancing Translational Sciences of the NIH under Grant Number UL1 TR001450. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Disclosures

The authors have no conflicts of interest to declare.

Ethical Approval

This study was reviewed, approved, and continuously overseen by the IRB at the Medical University of South Carolina (ID: Pro00067659). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

TrendyDigests

Crows Exhibit Counting Abilities Parallel to Human Toddlers, Study Finds

Posted: May 27, 2024 | Last updated: May 27, 2024

Crows, often depicted as harbingers of mystery in folklore, are now at the center of a revelation that may unravel the enigma of animal cognition. The term "birdbrained" might not be such an insult after all, as recent research reveals that crows can vocally count up to four.

These inquisitive urban birds can not only count but also match the number of calls they make when shown a numeral. This discovery, led by researchers from the University of Tübingen's animal physiology lab in Germany, highlights a process similar to how humans learn to count and recognize quantities. Published in the journal Science, these findings enhance our understanding of crow intelligence.

"Humans do not have a monopoly on skills such as numerical thinking, abstraction, tool manufacture, and planning ahead," said Heather Williams, an animal cognition expert and professor of biology at Williams College in Massachusetts. Although not involved in the recent study on crows, Williams emphasized that it's unsurprising that crows are intelligent. In the animal kingdom, numerical abilities aren't exclusive to crows.

Chimpanzees can count in numerical order and understand numeral values, similar to young children. Male frogs count competing calls to attract females, and scientists theorize that ants may retrace their paths by counting their steps, albeit imperfectly.

The research drew inspiration from the way toddlers learn to count, explained lead study author Diana Liao, a neurobiologist and senior researcher at the Tübingen lab. Toddlers use number words to count objects, so when they see three toys, their counting might sound like "one, two, three" or simply "one, one, one."

Liao speculated that perhaps crows could exhibit similar behavior. Her inspiration stemmed from a study in June 2005, which observed chickadees adjusting their alarm calls based on a predator's size.

Larger predators prompted fewer "dee" sounds, while smaller ones elicited more, indicating potential threats to chickadees. This observation intrigued Liao, who wondered if crows, known for their intelligence, could also have control over the number of sounds they produce.

Building on decades of research showcasing crow intelligence, Liao questioned whether they might possess the ability to effectively "count" through vocalization, akin to toddlers.

Over the course of more than 160 sessions, Liao and her colleagues trained three carrion crows, a European species closely related to the American crow. During these sessions, the birds learned associations between visual and auditory cues ranging from 1 to 4, and they had to produce the corresponding number of caws. For instance, a visual cue could be a bright blue numeral paired with the sound of a half-second drumroll.

The crows were tasked with matching the number of caws to the numeral shown, such as three caws for the numeral 3, within a 10-second timeframe after seeing and hearing the cue. Once they finished counting and cawing, they would peck at an "enter" key on a touchscreen to confirm their response. If their count was correct, they'd receive a treat.

Interestingly, the crows' reaction times lengthened as the cues continued, indicating that they were planning their caws before vocalizing. This observation suggests that the crows were strategizing the number of caws they would produce in advance.

The researchers could discern the number of calls the birds intended to make based on subtle acoustic differences in their initial call. This indicated that the crows not only grasped the concept of abstract numbers but also planned their actions accordingly to match the given number. "They understand abstract numbers … and then plan ahead as they match their behavior to match that number," explained Williams.

Even the errors made by the crows showed a level of sophistication. Whether they cawed one time too many, stumbled over the same number, or prematurely submitted their responses with their beak, Liao and her team could identify where the mistake occurred from the sound of the initial call. Williams noted that these errors resembled those made by humans, suggesting a high cognitive capacity in the crows' numerical abilities.

According to McGowan, the study revealed that crows are not merely reactive creatures but rather possess forward-thinking abilities and structured communication skills, suggesting a fundamental prerequisite for language development.

This insight into crow intelligence adds to decades of research in the field. Studies on New Caledonian crows, for example, have shown these birds creating compound tools to obtain food, indicating a capacity for rule establishment as observed in a November 2013 study co-authored by Andreas Nieder, lead researcher at the University of Tübingen lab. The complexity of crow communication, characterized by diverse tones and expressions, has long puzzled scientists, highlighting the richness of crow language.

Relevant articles: - These crows have counting skills previously only seen in people , Nature - Crows can count much in the same way as human toddlers, study finds , cnn.com - Crows can count much in the same way as human toddlers, study finds , CNN - Crows can count out loud, startling study reveals , Live Science

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• Open access
• Published: 23 May 2024

Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights

• Johayra Prithula 1 ,
• Muhammad E. H. Chowdhury 2 ,
• Muhammad Salman Khan 2 ,
• Khalid Al-Ansari 3 ,
• Susu M. Zughaier 4 ,
• Khandaker Reajul Islam 5 &
• Abdulrahman Alqahtani 6 , 7  

Respiratory Research volume  25 , Article number:  216 ( 2024 ) Cite this article

Metrics details

The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As respiratory-related admissions account for a substantial portion of pediatric illnesses, there is a pressing need to predict ICU mortality in these cases. This study based on data from 1188 patients, addresses this imperative using machine learning techniques and investigating different class balancing methods for pediatric ICU mortality prediction. This study employs the publicly accessible “Paediatric Intensive Care database” to train, validate, and test a machine learning model for predicting pediatric patient mortality. Features were ranked using three machine learning feature selection techniques, namely Random Forest, Extra Trees, and XGBoost, resulting in the selection of 16 critical features from a total of 105 features. Ten machine learning models and ensemble techniques are used to make accurate mortality predictions. To tackle the inherent class imbalance in the dataset, we applied a unique data partitioning technique to enhance the model's alignment with the data distribution. The CatBoost machine learning model achieved an area under the curve (AUC) of 72.22%, while the stacking ensemble model yielded an AUC of 60.59% for mortality prediction. The proposed subdivision technique, on the other hand, provides a significant improvement in performance metrics, with an AUC of 85.2% and an accuracy of 89.32%. These findings emphasize the potential of machine learning in enhancing pediatric mortality prediction and inform strategies for improved ICU readiness.

Introduction

Pediatric intensive care unit (PICU) mortality for respiratory diseases significantly impacts children’s lives and the healthcare system [ 1 ]. Such pediatric respiratory diseases as severe pneumonia, acute respiratory distress syndrome (ARDS), and respiratory failure, contribute to accounted for approximately 40% of PICU admissions, with a mortality rate ranging from 7 to 15% [ 2 , 3 ]. Pediatric mortality is steadily deteriorating on a daily basis, accompanied by an alarming decline in the infant survival rate [ 4 ]. Survivors of severe respiratory diseases in the PICU often experience long-term consequences like neurodevelopmental impairments, physical disabilities, and psychological issues. Approximately 25% of survivors of pediatric ARDS experienced new functional limitations six months after discharge [ 2 ]. PICU care for pediatric respiratory diseases incurs substantial healthcare costs [ 5 ]. The mean hospitalization cost for pediatric ARDS was approximately $67,000 [ 6 ], with an average ICU cost of $25,000 per day [ 7 , 8 , 9 ]. By investing in research, healthcare resources, and preventive measures, we can work towards reducing the impact of these diseases on children’s lives and alleviating the burden on the healthcare system [ 7 , 10 ].

Predicting pediatric mortality is of utmost importance in safeguarding young lives, enabling targeted interventions, and allocating resources to mitigate fatal outcomes [ 11 ]. Managing critically ill children with respiratory diseases demands significant medical resources, including ventilators, specialized medications, and skilled healthcare providers, which may strain the healthcare system, leading to potential shortages and increased costs [ 12 , 13 ]. The loss of a child in the PICU due to respiratory diseases has emotional and psychological impacts on families, caregivers, and healthcare providers, leading to long-term grief and mental health challenges. Early detection, effective management, and technological advancements are essential to mitigate these effects.

EHR data analysis and predictions based on machine learning models have gained popularity in recent years due to their ease of implementation and deployment [ 14 , 15 , 16 , 17 , 18 ]. The random forest model with an area under the receiver operating characteristic curve of 0.72 was used in an analysis at the Children's Hospital of Zhejiang University School of Medicine to predict postoperative mortality [ 19 ]. Another study at the University of Twente employed three classification models achieved an acceptable AUROC score of 0.71, underlining the need for additional study on methods for controlling class imbalance and model enhancement [ 20 ]. For newborns having major non-cardiac surgery, several research have developed postoperative mortality prediction models based on logistic regression [ 3 , 21 ]. Another study offers a simple but effective linear machine learning model with 11 key characteristics from a pediatric ICU dataset producing a predictive model with a ROC-AUC score of 0.7531 that beats current techniques like PRISM III (The Pediatric Risk of Mortality is a third-generation, physiology-based predictor for pediatric ICU patients [ 22 ]). The study highlights the improved efficacy and generalizability of their methods for forecasting pediatric ICU mortality.

Biochemical markers have become crucial in machine learning algorithms for accurate predictions of high-risk scenarios in pediatric patients. For instance, Early Plasma Osmolality Levels using locally weighted-regression scatterplot smoothing (LOWESS) to assess its relationship with hospital mortality, plasma osmolality at 290 mmol/L with in-, while levels below 290 mmol/L showed no significant association with mortality [ 23 ]. Serum magnesium levels were also studied, with an optimal range identified for the lowest mortality risk in critically ill children [ 24 ]. Furthermore, a study including albumin, lactate dehydrogenase, lactate, urea, arterial pH, and glucose develops a new scoring system for predicting in-hospital mortality in children outperforming the Pediatric Critical Illness Score (PCIS) showing higher AUC values in both the training and validation sets (0.81 and 0.80, respectively) [ 25 ].

Despite numerous studies on ICU mortality during COVID-19, research on pediatric populations using machine learning is limited, partly due to the scarcity of publicly available datasets. However, recently the PICU dataset [ 26 ] becomes publicly available which has made the possibility of investigating mortality prediction for different disease group. This paper focuses on enhancing mortality prediction accuracy in pediatric patients with respiratory diseases, integrating specific risk factors, biomarkers, and advanced modeling techniques.

Methodology

In this study, the publicly available PICU dataset [ 26 ] was utilized for data collection and to train, validate, and test different machine learning model. The initial dataset consisted of PICU database records and was filtered and preprocessed to remove outliers and repetitions. Three feature ranking approaches were explored to identify the optimal set of data for mortality prediction. To achieve more accurate outcomes in predicting mortality, various machine learning models, including Multilayer Perceptron (MLP) Classifier, Linear Discriminant Analysis, XGBoost Classifier, Random Forest Classifier, Logistic Regression, Support Vector Machine (SVM), Extra Trees Classifier, AdaBoost Classifier, K-Nearest Neighbors (KNN) Classifier, and Gradient Boosting Classifier, along with ensemble models, were applied to the preprocessed data. Given the highly imbalanced dynamics of the dataset (90.49% normal cases to 9.51% mortality cases), a subdivision sampling technique was implemented to obtain the most accurate predictions of mortality in pediatric patients. The prediction models for pediatric respiratory-related mortality were developed using Python software 3.9.13, and the Scikit-learn package was employed for implementing the supervised machine learning algorithms. Figure  1 displays a schematic representation of the methodology:

Step by step flowchart of the methodology

Data description

The PICU database comprises information collected during routine hospital care at The Children’s Hospital, Zhejiang University School of Medicine, from 2010 to 2019. This database follows the main schema of the MIMIC-III database but with localization-specific modifications. Standard codes, such as International Classification of Diseases (ICD-10) [ 27 ] codes for diagnosis, were used for frequently employed terms, and their English equivalents were derived. To ensure patient privacy, all identifiers required by the Health Insurance Portability and Accountability Act (HIPAA) of the United States were removed, resulting in completely de-identified patient data. The database contains a total of 13,944 ICU admissions and is structured into 16 tables [ 28 ].

Data preprocessing

The PICU database follows the framework of the MIMIC database, organized into tables for various information groupings. Before inputting this data into our machine learning model, preprocessing steps are necessary to format the database appropriately for training.

Data structuring

The database consists of 17 tables, with three dictionaries helping to interpret certain data fields, and two surgical data tables, which are not relevant to our research. Our dataset is derived from the information in the remaining 12 tables. For each patient admission case, diagnostic information is available, documented using ICD_10 codes. A mapping of ICD_10 codes to diagnose is provided in one of the dictionaries mentioned earlier. The diagnoses are categorized into admission, discharge, and clinical diagnostic categories. Additionally, the dataset includes information about the length of stay (LOS) in the ICU for each admission case, as well as physiological excretion and lab reports, which are mapped using the provided itemid (documentation of lab items mapped from the D_ITEMS table to numeric format) dictionary. The final dataset, constructed using these tables, comprises 13,941 instances and 592 columns.

Missing value removal

Out of the 592 columns used to construct the dataset, not all of them are relevant. Columns with a majority of missing data may introduce bias if imputed, so an iterative process is performed to discard columns lacking more than 70% of data. As a result, the dataset is reduced to 109 columns after discarding 483 columns.

After this reduction, each admission instance is evaluated within these 109 columns to check if the majority of column values are absent. Consequently, the initial 13,941 instances are further reduced to 12,841 instances (Fig.  2 ).

Proposed stacking ensemble technique with base models and meta-model

Filtering and outlier removal

In this study, we focused on respiratory system diseases in the diagnostic column, specifically using ICD-10 index J00-J99. Given the focus on pediatric patients, we also included congenital malformations of the respiratory system (ICD-10 index Q30–Q34). Additionally, four identifier columns were removed in this stage (Additional file 1 : Figure S1). As a result, the filtered dataset comprises a total of 1188 instances and 105 columns [ 29 ].

After filtering the data for our investigation, we conducted a detailed examination of the dataset to identify outliers. Outliers are values that do not align with medical norms as per published laboratory guidelines (Additional file 1 : Figure S2). Through a comprehensive iteration of the 105 columns in the filtered dataset, we removed values that exceeded the thresholds specified in Additional file 1 : Table S1.

Missing data imputation

Ensuring data completeness in the dataset is crucial for the success of this study. The dataset includes multiple demographic and medical biomarker data for each patient admission. However, some parameters may be missing for certain patients. Simply disregarding the available data can lead to the loss of valuable contextual information. To address this issue, data imputation is employed as an alternative to retain and fill in these missing values. Machine learning-based data imputation has been shown to be effective, and for this investigation, we utilized the MICE imputation technique [ 30 ]. Additional file 1 : Figure S3 illustrates the missing values for various characteristics in the dataset, with the spark lines on the figure’s right indicating data completeness.

Data splitting and normalization

To ensure unbiased model performance during training, the training dataset is divided into test sets using cross-validation, a well-established procedure. The entire dataset is split into 5 sets, each containing 80% training data and 20% test data [ 31 ].

For effective training of the machine learning model on the dataset, data normalization is essential to achieve generalized performance [ 32 ]. Normalization ensures that each feature contributes equally to the training process by transforming or scaling the entire dataset to a standardized range. Studies have shown improved performance when using normalized data for training instead of unprocessed data. In our study, we employed standard scalar to normalize the training data, and the scaling parameters were applied to the test set as well [ 32 ].

Data balancing

The dataset poses a fundamental challenge due to the class imbalance. While there are records for 1,075 cases (90.49%) that are alive, only 113 cases (9.51%) are deceased. This imbalance during training can introduce bias, leading the model to primarily recognize healthy cases. To mitigate this issue, a data augmentation method is proposed.

Data augmentation techniques are employed to provide synthetic data for minority classes. One such technique is Synthetic Minority Over-sampling Technique (SMOTE), a well-known method that generates synthetic data using the nearest kNN data point [ 33 ]. In our study, for both machine learning and ensemble techniques, the minority classes in the training sets are oversampled during augmentation to match the majority class.

Additionally, for the subdivision technique, each division is proportionally oversampled to achieve a balanced dataset. This approach helps address the class imbalance, enhancing the performance of the machine learning models and resulting in more accurate predictions.

Statistical analysis

The Chi-square univariate test and rank-sum test were employed to identify statistically significant characteristics between the two groups. The detailed description of this study is explained in Additional file 1 : S1. This analysis calculates the difference between the observed frequency (O) and the expected frequency (E) for each cell. It then squares the difference, divides it by the expected frequency, and sums the results for all cells in the contingency table [ 34 , 35 ].

Feature ranking

In the preprocessed dataset containing 105 features and a column with target variables, using all features may lead to overfitting and impractical deployment for real-time prediction. To select the most relevant features, three machine learning feature selection models are employed: XGboost, RandomForest and Extratrees. Descriptions of these feature ranking techniques are given in Additional file 1 : S2.

Using these feature selection models, we can identify the most relevant features to enhance prediction accuracy while avoiding overfitting and ensuring practical deployment in real-time scenarios.

Machine learning model development

This study explores several machine learning models from the Sci-kit learn library. We trained our data on MLP Classifier, Linear Discriminant Analysis, XGBoost Classifier, Random Forest Classifier, Logistic Regression, SVM, Extra Trees Classifier, Ada Boost Classifier, KNN Classifier, and Gradient Boosting Classifier [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. Notably, Extra Trees, Random Forest and Catboost classifier demonstrated the most promising performance. In the subsequent section, a comprehensive overview of these top-performing models is provided:

Extra trees classifier

Extremely Randomized Trees, or ExtraTrees (ET) Classifier, is a tree-based ensemble technique used in supervised learning. This model introduces extreme randomness in attribute values and tree node cutoff points. It is a subset of the RandomForest classifier, offering computational efficiency through more extensive randomization. The classification score measurement for ExtraTrees is a specific normalization of information gain. For a sample S and a division s, the measure is given by:

where \({H}_{s}(S)\) is the (log) entropy of the classification in S, \({H}_{s}(S)\) is the split entropy (also called split information by Quinlan (1986)), and \({I}_{c}^{s}\left(S\right)\) is the mutual information of the split outcome and the classification [ 42 , 46 , 47 ].

Random forest classifier

The Random Forest (RF) Classifier is a classification-focused machine learning algorithm that uses an ensemble approach by combining multiple decision trees. The term “random forest” comes from the fact that the algorithm creates a forest of decision trees with arbitrary constructions. Important division points in the data, like Gini impurity or information gain, are used to build decision trees based on different criteria. However, in Random Forest, the selection of split points is limited to a random subset of features at each node, rather than considering all features [ 39 , 48 , 49 ]. Additional file 1 : Figure S4 depicts the framework for the RandomForest Classifier.

Catboost classifier

CatBoost (CB) Classifier is a gradient boosting algorithm tailored for efficient handling of categorical features. By constructing decision trees and combining their predictions, it achieves accurate classifications. This specialized algorithm efficiently manages categorical features, feature scaling, and missing values, optimizing training performance. Compared to conventional gradient boosting algorithms, CatBoost offers a more streamlined and automated approach [ 50 , 51 ].

Stacking based machine learning model

Ensemble models are employed when individual models fall short of achieving desired outcomes [ 52 , 53 ]. This method has found extensive application, including in medical applications, where it proves effective in improving the accuracy of predictions by leveraging insights from various models [ 16 , 54 , 55 ]. Stacking ensemble technique is used in this study, combining the predictions of our top three models. Stacking ensemble, also known as stacked generalization, involves training a meta-model to optimally combine base models' predictions, resulting in improved overall performance. By utilizing input x and the predictions of the base-level classifier set M, a probability distribution is created, leading to a final prediction:

where ( \({{\text{c}}}_{1}\) , \({{\text{c}}}_{2}\) … \({{\text{c}}}_{{\text{m}}}\) ) represents the set of potential class values and \({{\text{P}}}^{{\text{M}}}\left({{\text{c}}}_{{\text{i}}}|{\text{x}}\right)\) represents the probability that example x belongs to class \({{\text{c}}}_{{\text{i}}}\) , as calculated (and predicted) by classifier M [ 52 , 53 ]. This investigation employs the classifiers Extra-trees, RandomForest, and CatBoost. The Gradient boosting classifier was used for the meta-model. Our proposed architecture for the stacking ensemble method is depicted in Fig.  2 below:

Data subdivision: an approach for highly imbalances datasets

The main challenge in our study is the significant class disparity, with a distribution of 90.49% to 9.51%, which can lead to biased predictions and an inability to accurately predict the minority class. To address this issue, we explore different techniques to mitigate data imbalance, namely undersampling and oversampling. Undersampling involves reducing the number of samples from the majority class to equalize class distribution. However, this approach results in the loss of valuable information, as a considerable percentage of data is discarded. On the other hand, oversampling aims to increase the number of samples in the minority class by duplicating data points, but applying this method to highly imbalanced datasets can lead to overfitting. The model becomes too reliant on the specific minority data points, leading to inaccuracies in predicting new data.

To overcome these challenges, we propose a subset method for handling imbalanced data in our study. We divide the majority class into three subsets and then create three Subdivisions by combining each subset with an oversampled version of the entire minority class. This division of the dataset into smaller Subdivisions helps reduce class disparity compared to the complete dataset. As a result, when oversampling is applied, it encounters a much lower discrepancy and generates fewer duplications of the minority data points, reducing the risk of overfitting. During the training process, we apply fivefold Cross-Validation for each Subdivision and use SMOTE to achieve class balance in the training set of each fold. The results of each Subdivision are later averaged to obtain the final prediction. This approach ensures that each Subdivision is given equal importance, and the ensemble of results helps improve overall performance. Figure  3 illustrates the data subdivision technique used in our study, depicting how the dataset is divided into Subdivisions, oversampled, and finally combined to achieve more balanced training data.

Data subdivision technique

By adopting the data subdivision technique, we aim to enhance the accuracy and reliability of our machine learning models in predicting the minority class while avoiding the pitfalls of traditional undersampling and oversampling methods. This innovative approach contributes to more robust and effective predictions in our study, paving the way for improved results in handling imbalanced data sets in various domains.

To balance the dataset, we divided the majority class into three subsets (359, 359, and 357 cases) and merged them with the minority class (113 instances). SMOTE was then used to achieve class balance.

Performance metrics

The receiver operating characteristic (ROC) curves and area under the curve (AUC), along with Precision, Sensitivity, Specificity, Accuracy, and F1-Score, were used to evaluate the performance of the classifiers. In addition, we utilized five-fold cross-validation, which results in a division of 80% and 20% for the train and test sets, respectively, and according to the fold number, this procedure is repeated five times to validate the entire dataset.

We utilized per-class weighted metrics and overall precision because the number of instances varied between classes. In addition, the AUC value was utilized as an evaluation metric. Five evaluation metrics (weighted sensitivity or recall, specificity, precision, overall accuracy, and F1 score) are represented mathematically in Eqs.  3 through 7 .

here true positive, true negative, false positive, and false negative are represented as TP, TN, FP, and FN, respectively.

Experimental setup

This study was carried out with the sklearn package and Python 3.9.13. All the models were trained with the specifications: Nvidia GForce 1050ti GPU, AMD Ryzen 7 5800X 8-Core Processor and 32 GB High RAM.

The statistical analysis was conducted using the scipy library and the chi-square test on our dataset. Demographic variables were excluded from the analysis, leaving continuous numeric columns. The chi-square rank-sum test was used to assess the statistical significance of individual characteristics for each group, with a significance threshold of P < 0.05. The dataset consisted of 1075 (90.49%) living cases and 113 (9.51%) deceased cases. The mean (SD) value of lactate for deceased cases was 9.99 (7.42), while for living cases, it was 3.63 (2.92). ALB/GLB and Chloride_Whole_Blood had P-values greater than 0.8, indicating no significant difference between the groups. The P-values for Creatine_Kinase (CK), Mean_Platelet_Volume (MPV), thrombin_time, Hematocrit, WBC_Urine, WBC/pus_cell, and Monocyte_Count ranged from 0.79 to 0.50. Additional file 1 : Table S2 presents the class-wise mean, standard deviation, and P-values for all biochemical markers and continuous variables.

In this study, three machine learning feature selection models were employed: XGBoost, RandomForest, and Extra trees. In the initial analysis, RandomForest yielded the most favorable rankings, resulting in higher accuracy scores for predictions compared to the other two methods. Out of the 106 features, the top 16 features were identified as the most effective for achieving optimal results with a minimal number of features. Figure  4 illustrates the F1-Scores for class 1 corresponding to the top features in our three best models.

F1-Scores for Class 1 across the top features

In Fig.  5 , the top 20 characteristics assessed by RandomForest are presented, and out of these, 16 were utilized. Among them, lactate was identified as the most significant characteristic.

Features ranked according to Random Forest feature selection algorithm

Machine learning model performances

The top 16 features, as ranked by Random Forest's feature importance attribute, along with the ‘HOSPITAL_EXPIRE_FLAG’ as the target variable, were used to train the algorithms. The models were then tested using fivefold cross-validation on the entire dataset. The performance of the top three machine learning models was investigated and evaluated. In the following section, we present and discuss the results of each experiment.

The ET classifier achieved an AUC score of 72.22% and an accuracy of 89.14%. However, its class-wise precision for the deceased class (class 1) was only 43.94%, indicating poor performance in accurately detecting the deceased cases. The RF classifier obtained an AUC score of 70.91% and an accuracy of 88.22%. However, when analyzing individual classes, the precision for class 1 was found to be 40.28%. The CB classifier demonstrates the highest AUC (77.11%) and accuracy (87.96%) among the three classifiers. However, it exhibits lower precision (41%) in predicting the deceased class compared to other classifiers. The stacking technique was employed to create an ensemble model by combining the top three performing models. The layered models were trained using gradient boosting classifier. As a result, the AUC score decreased to 60.59%, while the accuracy increased to 88.89%. Table 1 provides a summary of the results for the ET, RF, CB and stacking ML classifiers.

Figure  6 shows the confusion matrix for Extra Tree, Random Forest, CatBoost and stacking ML model. It is apparent that among these models CatBoost is performing the best in terms of sensitivity and AUC. However, none of the models are showing acceptable performance in this highly imbalance dataset (d). The ROC curves for ET, RF, CB and stacking ML model can be seen in Fig.  7 .

Confusion matrix for Extra Tree ( a ), Random Forest ( b ), CatBoost ( c ) and stacking ensemble method ( d )

ROC curves for Extra Tree ( a ), Random Forest ( b ), CatBoost ( c ) and stacking ensemble method ( d )

Data subset performances

Utilizing the top 16 features, we employ the CB classifier for the subdivision method. Dividing the dataset into three subdivisions, we independently train each subset on the CB model and then aggregate the results by averaging them. The subdivision method exhibits a noteworthy average subset accuracy of 89.32% with an AUC of 85.20%. The precision and sensitivity for this model are 77.98% and 77.29%, respectively, while the specificity and F1-score stand at 93.11% and 89.30%. For a visual representation of the model’s performance, refer to Fig.  8 , which illustrates the ROC curve for the subdivision method. The summary of the average result of the subdivision method and results for each subdivision is stated in Table  2 and 3 .

Confusion matrix for the subsets for the best performing model—CB Classifier and average ROC curve for the subdivision technique

The confusion matrix for each subset and average ROC curve are depicted in Fig.  8 .

The findings of this study showcase the significant potential of biomarkers in predicting mortality, offering valuable insights that can aid clinicians in making well-informed decisions. In our exploration of feature selection models for machine learning, namely XGBoost, RandomForest, and Extra tree, we discovered that the top 16 features selected by RandomForest yielded the most optimal results with minimal feature utilization during the initial investigations. This indicated that RandomForest outperformed its competitors in terms of predictive performance.

However, upon conducting further analysis, we unveiled certain limitations of the classifiers, particularly their inability to accurately predict the deceased class. Despite the promising results and efficiency of RandomForest in feature selection, it became evident that more advanced techniques were necessary to tackle the challenge of effectively predicting mortality in the dataset. This highlighted the importance of continually exploring and refining machine learning methodologies to enhance their predictive capabilities and address specific complexities in clinical scenarios. As such, our study not only underscores the significance of biomarkers in mortality prediction but also emphasizes the ongoing need for sophisticated algorithms to achieve more accurate and comprehensive predictions in critical healthcare settings.

We focused on the subdivision technique using the top 16 features for the CB classifier. Dividing the dataset into three distinct subsets, we proceeded to train each of these subsets independently on the CB model. Subsequently, the results were skillfully combined by averaging them, yielding a highly commendable average subset accuracy of 89.32%. Moreover, the AUC for this method achieved an impressive 85.2%, indicative of its robustness in discrimination capability. As a result of this approach, not only did we achieve superior accuracy, but we also observed significant improvements in precision, sensitivity, specificity, and F1-score, all of which are crucial performance metrics in medical predictive modeling. These outcomes underscore the effectiveness of the subdivision technique and its potential to further enhance the reliability and precision of our predictive model.

However, while the CB classifier excelled in predicting the living cases, it exhibited limitations when it came to accurately predicting the deceased class. The model struggled to achieve satisfactory performance in detecting the minority class of deceased cases, resulting in lower sensitivity and F1-score values. This indicates that additional research and further refinement are essential to enhance the model's ability to accurately predict the deceased class. To address these identified limitations, future investigations could focus on improving the handling of imbalanced data and exploring more advanced ensemble techniques or hybrid models that may provide a better balance between the two classes. Moreover, fine-tuning the feature selection process and incorporating domain-specific knowledge may also contribute to enhancing the model's predictive capabilities for the deceased class. A quantitative comparison among relevant studies is provided in Table  4 .

The data size in our study, encompassing 13,944 pediatric ICU cases, is comparable to that in Hong et al.’s study and larger than the datasets used in other referenced studies. This extensive data size provides a robust basis for our analysis and enhances the generalizability of our results. Our approach, focusing on feature engineering and data subdivision, yielded an accuracy of 0.8932 and an AUC of 0.8520. These results are notably higher than those achieved in the studies by Hu et al., Wang et al., and Zhang et al., indicating a strong predictive capability of our model. It is noteworthy that our study’s AUC is comparable to that achieved by Li et al., who employed advanced fusion models.

The variance in approaches and outcomes across these studies underscores the diverse methodologies in mortality prediction research. Our study contributes to this growing body of work by demonstrating the efficacy of feature engineering combined with data subdivision techniques in a pediatric ICU setting. This approach shows promise in enhancing predictive accuracy and could be a valuable addition to the clinician’s toolkit for mortality prediction, emphasizing the need for personalized and data-driven patient care. This comparative analysis not only positions our study within the existing research landscape but also highlights its potential clinical utility and relevance. By benchmarking our findings against these studies, we gain valuable insights into the evolving nature of machine learning applications in healthcare and identify avenues for future research and development in predictive modeling for pediatric respiratory diseases. The findings of this study need to be approached with caution due to the limitations posed by the relatively small dataset size and the class imbalance between deceased and living cases. The restricted sample size may impact the generalizability and robustness of the results. Furthermore, the class imbalance can introduce biases and hinder the accurate prediction of the minority class. To enhance the credibility and efficacy of mortality prediction models for pediatric patients with respiratory diseases, future research endeavors should focus on gathering larger and more balanced datasets. By increasing the sample size, the models can be trained on a more diverse and representative set of instances, leading to improved performance and better generalization to real-world scenarios. In addition to dataset size and class balance, researchers should also explore the incorporation of additional relevant features and biomarkers to refine the predictive models further. Integrating comprehensive and diverse patient data can enable the development of more comprehensive and accurate mortality prediction systems. Moreover, it is essential to conduct external validation of the developed models on independent datasets to verify their reliability and effectiveness in different healthcare settings. This validation process will provide crucial insights into the model’s robustness and its potential to be applied in diverse clinical environments.

Monitoring ICU patients’ parameters (lactate, pCO2, LDH, anion gap, electrolytes, INR, potassium, creatinine, bicarbonate and WBC) provide valuable insights into their pathophysiology i.e. medical progress and severity of critical illness, which help in guiding treatment or decision-making. The following explains the significance of the top parameters: elevated lactate levels indicate tissue hypoxia and anaerobic metabolism, often seen in shock or hypo perfusion states of ICU patients. Monitoring lactate helps assess tissue perfusion and response to treatment. Carbon dioxide (pCO2) is a byproduct of metabolism and is eliminated through respiration. Changes in pCO2 can indicate respiratory status and acid–base balance, especially in patients with respiratory failure or ventilation issues. Lactate Dehydrogenase (LDH) is an enzyme found in various tissues, including the heart, liver, and muscles. Elevated LDH levels can indicate tissue damage or breakdown, as seen in conditions like myocardial infarction, liver disease, or muscle injury. The elevated levels of LDH reflect the severity of critical illness. Whereas the anion gap is a calculated parameter that helps assess metabolic acidosis. An increased anion gap may indicate the presence of unmeasured anions, such as lactate, ketones, or toxins, which can be seen in conditions like diabetic ketoacidosis or lactic acidosis conditions that require extensive monitoring in ICU. Therefore, monitoring electrolytes like sodium, potassium, and chloride helps assess fluid and electrolyte balance, which is crucial in critically ill patients to prevent complications like arrhythmias or neurologic abnormalities. Potassium in particular is essential for proper cardiac and neuromuscular function. Abnormal potassium levels can lead to life-threatening arrhythmias and are often seen in conditions like renal failure or metabolic disorders. Bicarbonate is a buffer that helps maintain acid–base balance in the body. Changes in bicarbonate levels can indicate metabolic acidosis or alkalosis, which can occur in various critical illnesses. Creatinine is a waste product of muscle metabolism and is excreted by the kidneys. Elevated creatinine levels indicate impaired renal function, which is common in critically ill patients and can impact drug dosing and fluid management. Monitoring WBC (White Blood Cell Count helps assess the inflammatory response and immune function in critically ill patients. Elevated WBC counts may indicate infection or inflammatory processes. Similarly, monitoring PCT (procalcitonin) as biomarker of bacterial infections. Additionally, INR (International Normalized Ratio) is a measure of blood coagulation and is used to monitor patients on anticoagulant therapy. Changes in INR can indicate alterations in the coagulation cascade and may require adjustments in medication [ 58 , 59 , 60 , 61 ].

In summary, addressing the limitations of dataset size and class imbalance and incorporating advanced feature selection techniques and external validation can advance the accuracy and dependability of mortality prediction models for pediatric patients with respiratory diseases. These efforts will ultimately contribute to more effective and personalized patient care, leading to improved clinical outcomes for this vulnerable patient population.

In conclusion, this study sheds light on the promising potential of biomarkers in predicting mortality among pediatric patients with respiratory diseases, empowering clinicians to make well-informed admission decisions. Through meticulous evaluation of diverse classifiers, the CatBoost (CB) classifier emerged as the standout performer, exhibiting the highest AUC score and accuracy. However, the challenge lies in improving precision for the deceased class. By employing the stacking ensemble method, we were able to enhance overall accuracy, albeit at the expense of a slightly lower AUC score. Subsequently, the subdivision technique applied to the CB classifier using the top 16 features led to remarkable improvements in precision (89.32%), AUC (85.20%), and other essential predictive metrics. Overall, the CB classifier with the subdivision algorithm proved to be the most effective approach for mortality prediction. Looking ahead, our future objectives for this mortality prediction model in pediatrics encompass its seamless integration into clinical settings, especially in resource-constrained environments, and customization to suit the needs of specific populations. Additionally, we aim to incorporate real-time data streams to ensure up-to-date and accurate predictions. Collaborative efforts to enhance the dataset’s size and diversity are paramount to ensure the model’s robustness and generalizability. By diligently pursuing these avenues, we envision a significant impact on pediatric healthcare, as our model’s enhanced accuracy will bolster preparedness and improve patient outcomes, ultimately saving lives and benefiting young patients and their families.

Availability of data and materials

The preprocessed version of the dataset used in this study is available upon reasonable request to the corresponding author.

Divecha C, Tullu MS, Chaudhary S. Burden of respiratory illnesses in pediatric intensive care unit and predictors of mortality: experience from a low resource country. Pediatr Pulmonol. 2019;54:1234–41.

Article   PubMed   Google Scholar  

Ames SG, Davis BS, Marin JR, Fink EL, Olson LM, Gausche-Hill M, et al. Emergency department pediatric readiness and mortality in critically ill children. Pediatrics. 2019;144:e20190568.

Lillehei CW, Gauvreau K, Jenkins KJ. Risk adjustment for neonatal surgery: a method for comparison of in-hospital mortality. Pediatrics. 2012;130:e568–74.

Eisenberg MA, Balamuth F. Pediatric sepsis screening in US hospitals. Pediatr Res. 2022;91:351–8.

Balamuth F, Scott HF, Weiss SL, Webb M, Chamberlain JM, Bajaj L, et al. Validation of the pediatric sequential organ failure assessment score and evaluation of third international consensus definitions for sepsis and septic shock definitions in the pediatric emergency department. JAMA Pediatr. 2022;176:672–8.

Article   PubMed   PubMed Central   Google Scholar  

Papakyritsi D, Iosifidis E, Kalamitsou S, Chorafa E, Volakli E, Peña-López Y, et al. Epidemiology and outcomes of ventilator-associated events in critically ill children: evaluation of three different definitions. Infect Control Hosp Epidemiol. 2023;44:216–21.

Remick K, Smith M, Newgard CD, Lin A, Hewes H, Jensen AR, et al. Impact of individual components of emergency department pediatric readiness on pediatric mortality in US Trauma Centers. J Trauma Acute Care Surg. 2023;94:417–24.

Shamout FE, Zhu T, Sharma P, Watkinson PJ, Clifton DA. Deep interpretable early warning system for the detection of clinical deterioration. IEEE J Biomed Health Inform. 2019;24:437–46.

Marti J, Hall P, Hamilton P, Lamb S, McCabe C, Lall R, et al. One-year resource utilisation, costs and quality of life in patients with acute respiratory distress syndrome (ARDS): secondary analysis of a randomised controlled trial. J Intensive Care. 2016;4:1–11.

Article   Google Scholar  

Lee SW, Loh SW, Ong C, Lee JH. Pertinent clinical outcomes in pediatric survivors of pediatric acute respiratory distress syndrome (PARDS): a narrative review. Ann Transl Med. 2019;7:513.

Kortz TB, Kissoon N. Predicting mortality in pediatric sepsis: a laudable but elusive goal. J de Pediatr. 2021;97:260–3.

Mekontso Dessap A, Richard JCM, Baker T, Godard A, Carteaux G. Technical innovation in critical care in a world of constraints: lessons from the COVID-19 pandemic. Am J Respir Crit Care Med. 2023;207:1126–33.

Hughes RG. Tools and strategies for quality improvement and patient safety. In: Patient safety and quality: an evidence-based handbook for nurses. Agency for Healthcare Research and Quality (US); 2008.

Google Scholar  

Chowdhury ME, Rahman T, Khandakar A, Al-Madeed S, Zughaier SM, Doi SA, et al. An early warning tool for predicting mortality risk of COVID-19 patients using machine learning. Cogn Comput. 2021. https://doi.org/10.1007/s12559-020-09812-7 .

Rahman T, Al-Ishaq FA, Al-Mohannadi FS, Mubarak RS, Al-Hitmi MH, Islam KR, et al. Mortality prediction utilizing blood biomarkers to predict the severity of COVID-19 using machine learning technique. Diagnostics. 2021;11:1582.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Rahman T, Khandakar A, Abir FF, Faisal MAA, Hossain MS, Podder KK, et al. QCovSML: a reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model. Comput Biol Med. 2022;143: 105284.

Shuzan MNI, Chowdhury MH, Hossain MS, Chowdhury ME, Reaz MBI, Uddin MM, et al. A novel non-invasive estimation of respiration rate from motion corrupted photoplethysmograph signal using machine learning model. IEEE Access. 2021;9:96775–90.

Yang Y, Xu B, Haverstick J, Ibtehaz N, Muszyński A, Chen X, et al. Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning. Nanoscale. 2022;14:8806–17.

Hu Y, Gong X, Shu L, Zeng X, Duan H, Luo Q, et al. Understanding risk factors for postoperative mortality in neonates based on explainable machine learning technology. J Pediatr Surg. 2021;56:2165–71.

Markova BS. Predicting readmission of neonates to an ICU using data mining. University of Twente; 2021.

Stey AM, Kenney BD, Moss RL, Hall BL, Berman L, Cohen ME, et al. A risk calculator predicting postoperative adverse events in neonates undergoing major abdominal or thoracic surgery. J Pediatr Surg. 2015;50:987–91.

Pollack MM, Patel KM, Ruttimann UE. PRISM III: an updated pediatric risk of mortality score. Crit Care Med. 1996;24:743–52.

Article   CAS   PubMed   Google Scholar  

Wang H, He Z, Li J, Lin C, Li H, Jin P, et al. Early plasma osmolality levels and clinical outcomes in children admitted to the pediatric intensive care unit: a single-center cohort study. Front Pediatr. 2021;9: 745204.

Hong S, Hou X, Jing J, Ge W, Zhang L. Predicting risk of mortality in pediatric ICU based on ensemble step-wise feature selection. Health Data Sci. 2021. https://doi.org/10.34133/2021/9365125 .

Zhang Y, Shi Q, Zhong G, Lei X, Lin J, Fu Z, et al. Biomarker-based score for predicting in-hospital mortality of children admitted to the intensive care unit. J Investig Med. 2021;69:1458–63.

Zeng X, Yu G, Lu Y, Tan L, Wu X, Shi S, et al. PIC, a paediatric-specific intensive care database. Sci Data. 2020;7:14.

Anker SD, Morley JE, von Haehling S. Welcome to the ICD-10 code for sarcopenia, vol. 7. Wiley; 2016. p. 512–4.

Li H, Zeng X, Yu G. Paediatric intensive care database. PhysioNet; 2019.

October T, Dryden-Palmer K, Copnell B, Meert KL. Caring for parents after the death of a child. Pediatr Crit Care Med. 2018;19:S61.

Hegde H, Shimpi N, Panny A, Glurich I, Christie P, Acharya A. MICE vs PPCA: missing data imputation in healthcare. Inf Med Unlocked. 2019;17: 100275.

Mullin MD, Sukthankar R. Complete cross-validation for nearest neighbor classifiers. In: ICML; 2000. p. 639–46.

Singh D, Singh B. Investigating the impact of data normalization on classification performance. Appl Soft Comput. 2020;97: 105524.

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell earch. 2002;16:321–57.

Tallarida RJ, Murray RB, Tallarida RJ, Murray RB. Chi-square test. In: Manual of pharmacologic calculations: with computer programs. Springer Science & Business Media; 1987. p. 140–2.

Chapter   Google Scholar  

McHugh ML. The chi-square test of independence. Biochemia medica. 2013;23:143–9.

Taud H, Mas J. Multilayer perceptron (MLP). In: Geomatic approaches for modeling land change scenarios. Springer; 2018. p. 451–5.

Izenman AJ. Linear discriminant analysis. In: Modern multivariate statistical techniques: regression, classification, and manifold learning. Springer; 2013. p. 237–80.

Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H et al. Xgboost: extreme gradient boosting. R package version 0.4–2. vol. 1, pp. 1–4; 2015.

Breiman L. Random forests. Mach Learn. 2001;45:5–32.

Wright RE. Logistic regression. American Psychological Association; 1995.

Yue S, Li P, Hao P. SVM classification: its contents and challenges. Appl Math A J Chin Univ. 2003;18:332–42.

Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn. 2006;63:3–42.

Schapire RE. Explaining adaboost. In: Empirical inference: festschrift in honor of Vladimir N. Vapnik. Springer; 2013. p. 37–52.

Peterson LE. K-nearest neighbor. Scholarpedia. 2009;4:1883.

Natekin A, Knoll A. Gradient boosting machines, a tutorial. Front Neurorobot. 2013;7:21.

Wehenkel L, Ernst D, Geurts P. Ensembles of extremely randomized trees and some generic applications. In: Robust methods for power system state estimation and load forecasting; 2006.

Saeed U, Jan SU, Lee Y-D, Koo I. Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliab Eng Syst Saf. 2021;205: 107284.

Cutler A, Cutler DR, Stevens JR. Random forests. In: Ensemble machine learning: methods and applications. Springer; 2012. p. 157–75.

Biau G. Analysis of a random forests model. J Mach Learn Res. 2012;13:1063–95.

Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. CatBoost: unbiased boosting with categorical features. Adv Neural Inf Process Syst 31; 2018.

Dorogush AV, Ershov V, Gulin A. CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363 ; 2018.

Rokach L. Ensemble methods for classifiers. In: Data mining and knowledge discovery handbook. Springer; 2005. p. 957–80.

Opitz D, Maclin R. Popular ensemble methods: an empirical study. J Artif Intell Res. 1999;11:169–98.

Kwon H, Park J, Lee Y. Stacking ensemble technique for classifying breast cancer. Healthcare Inf Res. 2019;25:283–8.

Daza A, Sánchez CFP, Apaza O, Pinto J, Ramos KZ. Stacking ensemble approach to diagnosing the disease of diabetes. Inf Med Unlocked. 2023;44:101427.

Li H, Lu Y, Zeng X, Feng Y, Fu C, Duan H, et al. Risk factors for central venous catheter-associated deep venous thrombosis in pediatric critical care settings identified by fusion model. Thromb J. 2022;20:1–11.

Wang H, Liang R, Liang T, Chen S, Zhang Y, Zhang L, et al. Effectiveness of sodium bicarbonate infusion on mortality in critically ill children with metabolic acidosis. Front Pharmacol. 2022;13: 759247.

Caires Silveira E, Mattos Pretti S, Santos BA, Santos Corrêa CF, Madureira Silva L, Freire de Melo F. Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: a machine learning approach. World J Crit Care Med. 2022;11:317–29.

Vincent JL, Quintairos ESA, Couto L Jr, Taccone FS. The value of blood lactate kinetics in critically ill patients: a systematic review. Crit Care. 2016;20:257.

Jeong S. Scoring systems for the patients of intensive care unit. Acute Crit Care. 2018;33:102–4.

Schmidt GA. Evaluation and management of suspected sepsis and septic shock in adults; 2024. https://www.uptodate.com/contents/evaluation-and-management-of-suspected-sepsis-and-septic-shock-in-adults?search=ICU%20monitoring%20parameters&topicRef=107337&source=see_link

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This work was made possible by High Impact grant# QUHI-CENG-23/24-216 from Qatar University and is also supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2023/R/1445). The statements made herein are solely the responsibility of the authors.

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Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh

Johayra Prithula

Department of Electrical Engineering, Qatar University, 2713, Doha, Qatar

Muhammad E. H. Chowdhury & Muhammad Salman Khan

Emergency Medicine Department, Sidra Medicine, Doha, Qatar

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Susu M. Zughaier

Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, 56000, Kuala Lumpur, Malaysia

Khandaker Reajul Islam

Department of Biomedical Technology, College of Applied Medical Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia

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Conceptualization: JP, MEHC; Data curation: JP, KRI; Formal analysis: JP; Funding acquisition: MEHC, MSK, KA, SMZ, AA; Investigation: JP, MEHC; Project administration: MEHC, MSK, AA; Software: JP, KRI; Supervision: MEHC, MSK, AA; Validation: MEHC, KA, SMZ; Visualization: JP; writing—original draft: JP, MEHC, AA; Writing—review & editing: JP, MEHC, MSK, KA, SMZ, KRI, AA.

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Prithula, J., Chowdhury, M.E.H., Khan, M.S. et al. Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights. Respir Res 25 , 216 (2024). https://doi.org/10.1186/s12931-024-02753-x

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DOI : https://doi.org/10.1186/s12931-024-02753-x

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