How to Write and Publish a Medical Research Paper  

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How to Write and Publish a Medical Research Paper  

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Whether you are an aspiring undergraduate student or a medical student, conducting research and writing a manuscript can be a daunting yet rewarding task. However, with careful planning, some of these obstacles can be lifted. In this blog, we will show you how you can write and publish a medical research paper.

The first thing any prospective researcher should do is become familiar with the field they are interested in. This can be achieved through a thorough reading of current literature. That in itself can be challenging, but there are a few strategies to help you in this task! 

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The Initial Search – Google is your friend!

While it may be tempting to start your literature search with a more specialized search engine (Don’t worry, we will cover that in this blog post as well!), you might be surprised to hear that Google is a great starting point for initial searches! This is due to a couple of reasons:

  • Google’s algorithm has been trained on millions of topics. With that experience, it is more likely to predict the concepts you are searching for!
  • Following that train of thought, using Google can often take you to landmark papers (important for your basic knowledge) and novel papers (important for understanding cutting-edge work) related to your topic.
  • Google can also lead you to works not necessarily indexed on other research-focused search engines. This can alert you to newly occurring developments more promptly.
  • Finally, if you are new to the topic, Google can point you toward resources such as hospital websites, society guidelines, and other easy-to-read articles from reputable sources intended to orient you to your topic.
Using Google is as simple as using it for anything else! Type in the key phrases related to your topic. Here we type in ‘Congestive Heart Failure treatment efficacy’ and take a look at our results:
The very first search result is an article discussing novel treatment options for Congestive Heart Failure. Further down, you can also see an article printed in the American Heart Association journal, signifying that the article may be a landmark paper.

Finally, even further down, there is a website link to the Mayo Clinic’s information page on heart failure treatment, which may be worth browsing if you are relatively new to the topic! Once you have a working knowledge of what the key papers are in your field, you may want to move on to view more specialized literature, which can be achieved with the help of big medical databases!

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PubMed/Medline, Embase, and Cochrane – The Big Three

If you have worked in medical literature for any length of time, you will most likely be familiar with the three big databases: PubMed, Embase, and Cochrane. Searches conducted in just one of these databases can provide an excellent number of articles that highlight the cutting edge of research. In fact, the very first search result using Google that we have led to an article indexed on PubMed! Since PubMed-indexed articles are what you are most likely to find, we will focus on PubMed as the prime source of articles in this blog, using congestive heart failure as our field of interest. However, if you are conducting a systematic review, we recommend you check out our systematic review course HERE which discusses in detail the search strategy for systematic reviews.

It may be sufficient enough to use the search term ‘Congestive heart failure’ in PubMed to find thousands of articles. However, PubMed also has a few tools to help you refine your search to a more focused subset of articles, namely Medical Subject Headings (MeSH) terms and Boolean Operators.

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MeSH terms are standardized medical terms that PubMed uses to index each article. By using these terms for more obscure research areas, you avoid excluding articles that use alternate terminology, which can be a problem in research areas in which language is constantly evolving. For example, using the MeSH term for ‘Congestive Heart Failure’ will include terms such as ‘Cardiac Failure’ and ‘Heart Decompensation.’

At the same time, it is important to note that MeSH terms exist in ‘trees’ in which more specific topics are filed under more general topics. In our case, ‘Heart Failure’ is filed under ‘Heart Diseases’ and contains topics such as ‘Cardio-Renal Syndrome’ and ‘Dyspnea, Paroxysmal’. Using the MeSH terms wisely can help you narrow down your search to exclude things that you are not interested in, while still casting a wide net for what you are looking for.

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MeSH terms can be searched using the National Library of Medicine and can be added to your search term using the search builder on the right-hand side of the screen. Searching for “Heart Failure”[Mesh] provides >150,000 articles!

Now that we have learned how powerful MeSH terms can be, we can combine different MeSH terms using ‘Boolean Operators’. Simply, Boolean Operators are words such as ‘AND’ or ‘OR’ (capitalization is important) that you can use to join search terms representing different concepts. For example, if I wanted to search for heart failure articles that feature statins, I can search for (“Hydroxymethylglutaryl-CoA Reductase Inhibitors”[Mesh]) AND (“Heart Failure”[Mesh]). It is good practice to surround search terms with round brackets before using a Boolean Operator!

Some additional tips to remember when conducting your literature search:

  1. Use other tags: [Mesh] is just one of the different tags that you can use next to a search term. Try narrowing down by an author you are particularly interested in using [1au] next to their name to find articles in which they are the first author. Trying to find something in a particular language? You can use the [la] tag to do so. For more information, visit the PubMed help page to learn about other search tags!
  2. Search by Top Journals: Most often, one can be overwhelmed by the number of articles that need to be parsed. Simplify your search by first reading articles published in high-impact factor journals to read high-quality data that has been extensively peer-reviewed. For example, the Journal of the American College of Cardiology is a great place to start for cardiovascular diseases!
  3. Use Reference Lists of Top Articles to find other Top Articles: Often, research builds on itself. Take advantage of the articles you find in top journals by looking through the works that are cited within the article! PubMed pages also feature ‘Comment in’ (Articles that comment on this work), ‘Similar Articles’, and ‘Cited in’ (Articles that cite this work in their references) sections for each indexed work, which can also lead you to other key papers!

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Formulating a Research Hypothesis – the PICO approach

So now you are well-versed in your topic of choice, what next?

To conduct new medical research, you need to first identify a gap in the literature that needs to be addressed. For example, perhaps there are few studies on a specific side-effect of a commonly used medication in heart failure or there are now controversial reports of a certain intervention that needs to be studied more.

Identifying what needs to be studied is more likely to get eyes on your work! This can be as simple as validating a prior study on a greater number or diversity of patients or as complex as studying patients with rare forms of disease.

Organizing your questions using the PICO approach (Population, Intervention, Comparison, and Outcome) can help with secondary literature search and help decide what data you need.

Population

Simply, try to list the characteristics of the patient population that you want to study e.g. are you interested in studying adults or children? What stage of heart failure? Have they already had some sort of complication?

For example, you may choose to study adults greater than 65 years of age with end-stage heart failure and comorbid kidney failure. Be careful about how particular you want to be. Hyper-specific patient populations can dampen the generalizability of your results, whereas patient populations that are too broad do not contribute to tailored care.

Intervention

Next, list the intervention that you are interested in studying. Perhaps it is a new medication specific to heart failure, a new form of exercise or lifestyle modification that has recently shown positive results, or it may be a new surgical procedure!

Comparison

What alternative to the intervention are you considering? Perhaps it may be a placebo pill or another medication in the same class. Perhaps you will compare the exercise intervention to those who remain sedentary or utilize alternative exercises.

Finally, you may consider non-intervention when compared to surgical procedures, or alternative approaches (laparoscopic vs. open approach).

Outcome

Finally, what outcome do you plan to measure? This may be as simple as the percentage of mortality following a certain specified follow-up period, or the occurrence of a certain side effect. For surgical procedures, this could also include time to discharge or re-admission status.

With all these in mind, one can form a cohesive question to answer. In our example, this might be the following: Do statins (intervention) improve 10-year mortality (outcome) in heart failure patients over the age of 65 (population) over patients taking no statins (comparison)?

Once you have a working hypothesis, it helps to return to your literature search to narrow down articles that have looked at adjacent populations, interventions, comparisons, and outcomes to look at the types of data and statistical analyses used.

Organize your references using a citation management tool such as EndNote or Mendeley!

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Data, Data, and more Data!

What data should you collect?

Now that you have a working hypothesis, the next thing to do is formulate the type of data that you will acquire. For most analyses, it is not enough to simply consider the dependent variable (outcome) and the independent variable (intervention). Oftentimes, there are confounding and mediating factors: factors that affect the relationship between your dependent and independent variables.

For example, suppose you were to study the effect of a medication on heart failure patients. This medication is known to be metabolized by the liver. If your sample has a significant number of individuals with concurrent liver failure, the efficacy of the medication may be altered and give you biased results. To address this concern, liver failure must be accounted for in your statistical approach.

Compiling a list of confounding and mediating factors may sound difficult. However, after your literature review, you should have a good understanding of the different factors you may need to keep in mind. Other studies may have used some of these factors in their statistical approach, or they may have listed the ones they couldn’t account for in their limitations!

Other data that you might want to gather includes a comprehensive description of the individuals you may be studying. This includes age, race, sex, and other characteristics that provide ‘face value’ to your research i.e., you can show a good sample that is not biased unintentionally.

Finally, you must also decide the nature of the data you are collecting. For example, suppose we want to collect data regarding the Body Mass Index of our sample. Would it be more appropriate to collect this data on a numerical scale? Or should we simply classify our participants based on pre-defined BMI classes e.g., <18 kg/m2, 18-25 kg/m2, etc.?

Perhaps we also intend on using the height of the participant in our parts of our analyses, so we can record the weight and heights separately and calculate the BMI later ourselves? These considerations are important, especially if new data must be collected.

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Where to get data!

Data can come from many places. The local lab that you are working with might have data readily available for you to analyze.

For clinical research, the most common method of data collection is to conduct chart reviews, particularly if you are writing case reports, case series, or case-control studies. These can be electronic or paper records, depending on what your hospital system prefers.

For prospective studies such as cohort studies or randomized control trials, you may have to collect data yourself, either through a primary survey conducted on paper, physical examinations, or through phone calls.

Finally, you may also choose to run your analysis on large publicly available databases. These large databases often require a rigorous application process that prevents scientific overlap and promotes collaboration and can thus introduce you to other experts in the field.

Regardless of which of these routes you take, be sure that any data collection, curation, analysis, or publication occurs with the express consent of the owners of the data (your lab or the host of the publicly available database) and/or your local internal review board (particularly if you are collecting the data yourself).

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Statistical Analysis – Choosing the Correct Approach

T-tests or Chi-square? Linear regression or a Cox Proportional Hazards model? Choosing the right way to analyze your data can be a tough thing to do. Thankfully, by asking yourself a few questions, you can often make the right choice.

  1. Is your data cross-sectional or longitudinal?
  2. Is your outcome categorical or numerical?
  3. Is your main predictor categorical or numerical?

These questions can help you choose the correct analytic approach. If you would like to learn more about how to do statistical analysis for your research projects, check out our statistics course here.

Overall, there are a couple of things to keep in mind. Most robust manuscripts will require a detailed descriptive table of the involved subjects. The table may also divide the subjects into several groups of interest, allowing tests such as the t-test, chi-square, or ANOVA to compare other characteristics between these groups.

Next will come the main hypothesis-testing portion of your analysis. We implore you to discuss with your primary mentor and/or statistician to learn and apply the correct analytical technique to test your hypothesis.

After the primary analysis, secondary analyses may be warranted to further confirm your hypothesis. Robust analyses will also check if the model holds up in your particular circumstance e.g., for time-to-event analysis using Cox Proportional Hazards models, does your model follow the proportional hazards assumption, does it include non-linearity, or does it include particularly influential observations?

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Writing a Manuscript – Where to Start?

Once you have results that you are confident in (Key Point: confident! Results do not need to be statistically significant, and purposely manipulating them to appear that way constitutes academic fraud! Instead, be confident that you used the right technique with sound data!), it is time to start writing up your manuscript!

One pro tip: If you or a mentor are targeting a specific journal for publication, it would be beneficial for you to go through the journal’s author guidelines. Specifications for word count, structure, citations, and figures can help you package together your manuscript efficiently.

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Methods

We recommend you start with the more concrete parts of your research i.e., the methodology and the results. Methods should begin by describing your participants: How were they sampled? What inclusion criteria or exclusion criteria were used? Using a diagram to show the selection process at each stage is helpful to the reader.

Afterwards, it is important to list out the data that was collected, and how it was organized. Was a standardized survey used to obtain data? Were objective metrics such as height or weight measured by the investigators or self-reported? When complex procedures were employed, what was the protocol for it e.g. imaging protocols, surgical techniques, or medication/intervention usage? If patients were monitored or followed up, you must also describe the interval at which it occurred.

The final portion of the methods must describe the statistical techniques used to analyze the data that was collected. Assumptions of normality, how the descriptive data was organized, and how the hypothesis was collected should be listed here.

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Results

Start the results section by describing your participant sample. The basic distribution of demographics and variables of interest should suffice here. Describe statistically significant differences as well if tests of comparison were used to compare two groups of participants.

The next few paragraphs should focus on answering the question in your hypothesis. Did your analysis show anything of statistical significance? Were there any general trends that need to be discussed?

If you elected to conduct a secondary analysis, what result in the primary analysis prompted you to do so? And finally, did the model or technique you chose hold up to its required assumptions? Make sure to employ easy-to-understand graphs and figures to illustrate your results so that readers can grasp your concepts at a glance!

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Introduction

Next, we move to the more conceptual part of the manuscript, the introduction. While writing this section of your manuscript, ask yourself: How did I arrive at the hypothesis? Suppose we have a clinical question regarding the efficacy of a drug for a certain medical disease.

Start by describing the disease that you were interested in. What is it, what is the general burden of disease, and how much does it affect the healthcare system financially? This puts into perspective for the reader why researchers need to tackle this topic.

Next, describe how the disease is currently being treated. What is the gold standard treatment? How does it affect disease outcomes? What are the new and upcoming treatments? What are the concerns with such treatment?

Now, you can narrow your focus to the drug you are interested in. What do current studies have to say about the drug’s efficacy? This provides crucial background to reviewers shows that you have an excellent grasp of the topic and sets the stage for the final portion of the introduction: identifying the gap in literature for the reader. We discussed this in detail in the section on literature review and hypothesis forming. List out how you will address the current gaps in the literature, thus contributing to the literature.

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Discussion

Discussion writing is probably the most difficult task of any manuscript. At the same time, it is the most scientific and ultimately rewarding. Structuring your discussion in the following way can help convey the complex concepts that you are trying to convey!

Start by summarizing the main results of your study in a way that answers the study question! Then state whether your results align with your initial hypothesis. Emphasize the strengths of your study with these few sentences if possible, such as the diversity of your sample or your analytical technique. Try to keep it around 2-3 sentences at most.

The following few paragraphs should place your results in the context of existing literature. Do other seminal works related to your topic suggest a similar association as what you found? If not, are there any particular reasons why? This is another place where a strong review of literature can help the reader place your work in a broader context.

Next, describe the implications of your work on future studies. Have your results raised further questions that need to be answered? Have you described a new technique that must now be used to evaluate older data? As we discussed before, new science is based on old science. Providing the next steps for future investigators is crucial to impart the importance of your work.

Finally, describe what the limitations of your work are. Perhaps you had a smaller-than-desired sample size or were unable to incorporate a certain variable of interest in your analyses due to logistical restraints. No work of science is perfect and acknowledging the shortcomings of your work (and possible ways you were able to address them) provides readers and reviewers with what they can expect from future works.

Often, a conclusive paragraph is warranted to provide your readers with a final take-home message. It is important here (and everywhere else in the manuscript!) not to exaggerate the importance of your work.

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Proof-reading

Once you have an initial draft ready, it is important to have it meticulously reviewed and edited by all members involved in the project. With the help of other experts, you will be able to further strengthen the key points of your manuscript and add further clarity to your methodology. This may require multiple rounds of edits, but it is well worth the effort.

Journal Selection, Submission, and Revisions

Once you have the approval of all relevant authors, congratulations! You are now ready to submit your article to a relevant journal. Choosing the appropriate journal to submit to is also a very important decision to make. Here are a few tips on choosing the right journal for you and to get your work visibility:

  1. First and foremost, make sure that the journal you decide to submit accepts your type of research. Some journals may be limited to case reports, systematic reviews, etc.
  2. Make sure your topic aligns with the aims and scope of the journal. Some journals may be hyper-specific such as only accepting research about therapeutics, whereas others may have broader scopes focusing on any research regarding a certain disease.
  3. Consider the impact factor of the journal. The impact factor is a metric showing how often articles within the journal get cited. If your research is relatively novel and has few limitations, it may be worth submitting to a journal with a higher impact factor that provides your work with more exposure.
    However, if you are on a lower budget or your research is in a relatively well-explored field, aim for a journal with more modest impact factors as it makes your work more likely to receive consideration for review.
  4. If you are submitting original research on a budget, you may want to avoid Open-Access Journals. These journals allow readers to read your research for free (which is a great way to increase visibility) but instead charge submitting authors upon acceptance.

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If you have not already, you may need to forward your manuscript according to the journal’s author guidelines! Once under review, you may receive a decision from the journal with several decisions:

  1. Acceptance Pending Minor Revisions usually entails no major shifts in research direction for acceptance. These may include requests for additional figures, minor clarifications, and grammatical changes.
  2. Acceptance Pending Major Revisions usually requires some shifts in research direction. Reviewers may ask for additional analyses, or re-analysis using different statistical techniques. Additionally, reviewers may ask authors to reconsider their stance regarding the impact of their work and future directions in the discussion section.
  3. Rejection/Rejection with the Option to Resubmit usually means that the journal is unwilling to publish your article even with substantial revisions. Do not be concerned! Often, if the article was rejected after peer review, you will receive valuable feedback that you can incorporate into your research for submission to another journal!
  4. Acceptance in Current Form means you hit the nail on the head! The article will be sent to the scientific editor and proofing team for preparation for publication.
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Conclusion

Research can be a difficult and rewarding experience! Tackling different aspects of research from literature search to manuscript submission can seem very daunting.

That is why we created a practical course on how to write and publish research projects. It is 100% risk-free, you get your money back if you are not satisfied. Give it a try here.

Hopefully, with our help, you can hit the ground running regardless of where you are in the process!

Wish you the best of luck on your research journey.

Hamza Ibad, MBBS

Dr. Ibad graduated from the Aga Khan University Medical College and completed a post-doctoral research fellowship at Johns Hopkins in the Department of Radiology (Musculoskeletal Division). Dr. Ibad’s research and clinical interests include deep-learning applications for automated image interpretation, osteoarthritis, and sarcopenia-related health outcomes.

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