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“Iatrogenic comorbidity” is one of those jargon phrases that makes me want to run far far away from research.  To me, it’s code for this is an article for those who have letters after their name and if you don’t you’re too plebian to understand. For regular folk, it’s a term that’s difficult to remember, impossible to pronounce, and seems to have something to do with death.

It has nothing to do with death. In layman’s terms, iatrogenic comorbidity is illness or disease caused by medical treatment which results in two or more simultaneous conditions in a patient. It is also one of the most important (and overlooked) aspects of treating depression and prescribing antidepressants. If more patients understood what it meant, perhaps more doctors would be forced to take it into consideration.

Let’s break that down even further.

Iatrogenic is an adjective that means, “relating to illness caused by medical treatment or examination.” For example, if a woman has heart surgery and the stitches get infected, the infection is an iatrogenic effect. If the stitches never existed, she wouldn’t have an infection.

Comorbidity means the “simultaneous presence of two or more chronic diseases or conditions in a patient.” For example, an elderly person could have osteoporosis (brittle, porous bones) and dementia at the same time.

Putting the two words together, iatrogenic comorbidity is what happens when medical treatment or examination causes two or more chronic diseases or conditions. In the case of our heart patient, let’s say that she was given antibiotics to fight against the iatrogenic effects of the infected stitches, but that she didn’t know she was allergic to the particular antibiotics. When she takes the drugs, she goes into anaphylaxis. Now, the heart issue, the infection, and the anaphylaxis are all comorbid conditions. A good physician needs to carefully understand what caused what issue in order to properly treat it, otherwise, he might misdiagnose and mistreat.

I am not a doctor, but I imagine it’s generally easier to trace iatrogenic comorbidity in physical illnesses. The heart surgery results in infected stitches which results in anaphylaxis. It’s an unpleasant outcome, but the progression is clear. Mental health, on the other hand, is inherently fuzzier. It is not uncommon for patients to present with comorbid conditions, like depression and anxiety. When medication is administered and more conditions show up, like suicidality, there’s no real way to know what caused what. Did the medication cause the patient to want to kill himself? Or would the urge have developed had the medication not been given? Was it the chicken? Or the egg?

A fancy term for a common problem.

There is a growing faction of psychiatrists and researchers who are calling for a drastic overhaul of the way we prescribe antidepressant and antianxiety drugs because of the risks of iatrogenic comorbidity. General practitioners, in particular, are being called out for defaulting to prescription antidepressants rather than recommending therapy. The argument, essentially, is that general practitioners are well…generalists. They are the traffic control of healthcare, designed to guide people down the appropriate specialist highway so oncologists don’t get bogged up with common colds. In theory, this means that GPs should refer someone suffering from depression to a psychologist for further evaluation. In practice, what often happens is that GPs prescribe an antidepressant (or multiple antidepressants) and send the patient on their way.

To put this practice in perspective, I lived in New York City for eight years and never once saw a psychiatrist for my Effexor XR and Wellbutrin XL. Furthermore, my GP only required that I see him once every 12 – 18 months, for a five-minute appointment. So over the course of nearly a decade, I got about thirty minutes of face time with the man who prescribed me daily psychiatric drugs. That’s fucking absurd.

So why is this happening? A general practitioner would never give a patient a script for chemotherapy, so why is it a widely accepted practice when it comes to depression and anxiety?

I would argue that a major factor is the fact that the iatrogenic comorbidity of chemotherapy is much more obvious and well-studied than it is with antidepressants. We know that chemo is a hell of a drug because it quickly makes most people vomit, turn grey, and lose all their hair. The iatrogenic effects of antidepressants though, do not develop in a common, linear way—if they develop at all. The same drug presented to four people with similar symptoms, background, and genetic makeup can produce four very different effects. One person may gain weight and develop insomnia. The second might sleep well but experience PSSD (post-SSRI-sexual-dysfunction.) The third may lose weight and have suicidal tendencies. The fourth may flourish for a few months, but show symptoms of bipolar disorder years later.

In all of these cases, when the patient goes back to their general practitioner because they’re not sleeping well or their weight has changed or they’re suddenly manic, what’s likely to happen? They get a sleeping pill or they’re put on a diet or they add a Lexapro back to their Celexa. Now they’ve got additional medication in the mix, which creates the potential for even more iatrogenic effects. And so on and so forth, until the patient is drugged up to their eyeballs and their system has gone haywire.

Does this happen every time? No. But it happens enough, and it’s avoidable if protocols are put in place to make it more difficult to prescribe antidepressants. I’m baffled by the fact that a course of some sort therapy is not considered a pre-requisite to prescribing antidepressants, especially given that research indicates that over the long term, therapy is just as, if not more, effective than antidepressants. Additionally, the positive effects are more likely to endure and there is little risk of iatrogenic comorbidity.

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I was scrolling through Twitter when I came across a tweet by Michael P. Hengartner, PhD that read, “In my new open access paper, I critically discuss whether long-term antidepressant use has prophylactic effects, ie., whether long-term use effectively protects against depression relapses and why we must consider withdrawal reactions…”

Hengartner is one of my great new finds on Twitter. He’s a senior lecturer and research in evidence-based medicine with a focus on public health and social psychiatry. He is openly critical of the modern psychiatric system and its practices not because he’s anti-psychiatry, but because he’s pro-drug safety. His initial research on depression raised questions regarding treatment rates and long term outcomes, which combined with his research around selective reporting and flaws in the scientific process, led him to realize that antidepressant efficacy is likely overestimated and that the negative side effects have been underreported and minimized. Effectively, he’s stumbled across what many patients (including myself) have said all along.

But the difference between Hengartner and a huge faction of researchers and psychiatrists is that he isn’t’ turning his findings into an Us vs. Them debate. Instead, he’s acting like a fucking scientist and questioning our existing assumptions which is the whole point of science and advancing medicine in the first place. Questioning, analyzing, and building upon existing research is how we move forward. It is not anti-psychiatry or anti-antidepressants. It’s asking tough, critical questions to make sure that we are doing right by patients.

Though Hengartner is doing meaningful work that might actually change something, I still find myself scratching my head at some of his tweets. His audience, I assume, is mostly psychiatric professionals, so he has no reason to dumb down industry language for laypeople like me. A world like “prophylactic” makes me want to run to the nearest Buzzfeed listicle that provides about as much intellectual value as a bag of stale rice cakes. Still, I am trying to gain a deeper understanding of psychiatric and pharmacological research, so down the prophylactic rabbit hole we go.

prophylactic

pro·​phy·​lac·​tic | \ ˌprō-fə-ˈlak-tik also ˌprä- \

adjective

  1. guarding from or preventing the spread or occurrence of disease or infection
  2. tending to prevent or ward off: PREVENTIVE

noun

  • Definition of prophylactic
  • : something prophylactic
  • especially : a device and especially a condom for preventing venereal infection or conception

I’m having flashbacks of someone referring to condoms as a prophylactic, so I guess I should have put two and two together…moving on!

In the case of pharmaceuticals, prophylactic drugs are medications or treatments designed and used to prevent a disease from occurring. Antibiotics taken to prevent infection before surgery are a good example, as well as drugs taken at the first sign of a migraine that keep debilitating symptoms at bay. Hengartner’s recent article examines the prophylactic use of antidepressants for depression, presumably in response to Saeed Farooq’s systematic analysis claiming that using antidepressants as a pre-emptive measure could help to prevent depression.

Hengartner’s interprets the existing research differently. He points out an often overlooked aspect of antidepressant discontinuation studies: antidepressant withdrawal. Withdrawal symptoms can appear erratically and don’t manifest universally across all patients. They are often confused with relapse, which according to Hengartner, compromises the validity of discontinuation studies.

He says, “It is difficult to quantify the extent to which events recorded as depression relapse in maintenance studies are related to withdrawal reactions, but different estimations suggest that it is presumably the majority.”

In short: We can’t know whether or not antidepressants could be considered prophylactic (or rather, the condom of mental health) because a relapse in depression and side effects of antidepressant withdrawal is often confused, misinterpreted, and misdiagnosed.

The more you know. Ding ding dong!

* * *

Click here for Hengartner’s article, “How effective are antidepressants for depression over the long term? A critical review of relapse prevention trials and the issue of withdrawal confounding”

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When people describe legitimate research, they tend to preface it with the term “peer review.” Because peer review is a critical part of scholarly publishing, it’s worth taking a few hundred words and diving into its meaning.

What is peer review?

Peer review is exactly what it sounds like: academic peers review an individual’s work in order to determine if the research is strong enough to publish. All articles published in legitimate research journals are peer-reviewed, which is why scholarly journals are deemed a reliable source of information. This is also why predatory journals are a problem. They don’t follow the peer review protocol, which means there aren’t any gatekeepers to stop unethical or fraudulent research from getting out into the public.

How does peer review work?

Peer review follows a standard process:

  • An individual or group of people complete a study, write an article, and send it to a journal. It doesn’t matter if its original research or a systematic review. If the work is going to a journal, it will be peer reviewed.
  • The journal editors send the article out to other scientists in the field. Typically, the work is sent blind, which means that the author(s) (and sometimes the reviewers) remain anonymous during the review process. This helps keep bias to a minimum, though it’s not a perfect process. I’ve been at multiple dinners with Justin (my professor boyfriend) and his colleagues when over the course of shooting the shit, they admit that they were reviewers for each other’s work. It didn’t matter that the review was blind. Academic focus is so narrow that it creates tight-knit communities where everyone knows everyone. Topic and writing style can be as good as a name tag.
  • The reviewers provide feedback for the author and tell the journal editor whether or not they think the article is fit for publication.
  • If the work is considered to be of high quality, the authors are invited to revise and resubmit the article for consideration.
  • In theory, only articles that meet scientific standards are considered for publication. This means the work must be ethical, acknowledge other work in the field, backed up with evidence, well reasoned, and with disclosed conflicts of interest.

Is all research peer-reviewed?

If you find research in a reputable journal, the article has been peer reviewed. However, sometimes researchers bypass the peer review process and instead submit research directly to their university or for use at an industry conference.

How difficult is it to get published?

Having watched Justin go through multiple rounds of article submission, I feel the need to highlight the difficulty and glacial pace of publication. This shit is hard and slow. Justin has work he finished years ago that has only recently been accepted. It’s not that it takes all that long to read a paper, but because reviewers aren’t paid and they have other things to do, sometimes the work gets lost in the slush.

One survey suggested that 50% percent of articles are ultimately published, but only 9% are accepted without a revise and resubmit. While 50/50 odds aren’t the worst, the competition for publication in top journals is vicious. The journal Science only accepts 8% of submissions, while the New England Journal of Medicine publishes just 6%.

Is peer review a perfect system?

In short, no. Critics of the peer review system say it’s slow and expensive, inconsistent and subjective, and often filled with bias and abuse. However, with no viable alternative, both researchers and the general public must continue to believe in the system. The irony of course, as summed up by peer review critic Richard Smith: “How odd that science should be rooted in belief.”

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Click here for Part One of Where to Find Scientific Research Papers (and How to Know if They’re Legit).


Yesterday, I wrote about predatory journals. I suppose I shouldn’t have been surprised to learn that there are shitty people in the research world who get off on exploiting academics and undermining science, but I was. Blame my mother. She raised me in a world where all people, on some level, are good. I never quite bought it, but I also didn’t learn to look at everything and everyone with skepticism. I tended to assume that people were just doing the best they can. They may be severely annoying in the process, but ultimately it was all with good intention.

The internet has shattered that illusion. People are fucked.

And so the burden falls on to the individual to see through the bullshit. Historically, we’re not great at that, but when it comes to sussing out whether or not a research paper is legitimate, there are a few quick and easy ways to verify your science.

Check the Citations

Google Scholar is one of my favorite ways to source research, but because Google Scholar is a search engine and not a curated database, articles published in known predatory journals may pop up in your search results.

The quickest way to determine if the article is legit is to check the “Cited by” number at the bottom of the search. If an article has multiple citations, it means other researchers are referring to the research in their own articles, which indicates legitimacy. It’s rare that articles are cited hundreds of thousands of times like Eugene Paykel’s excellent study in the photo above. (Paykel’s study is the research equivalent of a New York Times bestselling book.) According to my smarty-pants academic boyfriend Justin, even mid-single digits is enough to assume the research isn’t bunk.

Journal Ranking

While citations are a great place to start, they benefit from time in the system. Paykel’s article has been around since 1976, which means it has nearly half a century of research built upon it. New research won’t come with shiny citations, so you need to look at the journal it’s published in to see if it’s legitimate.

Academic journals are ranked for impact and quality. Think of it like the college system. Harvard isn’t the same as Iowa State, but that doesn’t mean that Iowa State isn’t capable of producing damn good citizens (and we all know question marks who graduated from top tier universities.) The top journals produce great work, but there is still plenty of meaningful work to be found in smaller journals.

Find journal rankings by googling the name of the journal and the word “ranking.” The Scimago Journal & Country Rank (SJR) should be the first result, and that will take you to a list with the journal in question buried somewhere in there. The rank is determined by the H-Index, the details of which I don’t entirely understand. The H-Index is determined by the number of publications and citations, and higher H-Index indicated a higher ranking. However, the H-Index is not standardized across subject areas, so you can’t cross-compare.

For our purposes, the H-Index doesn’ matter too much. In Justin’s words, “A low ranking isn’t necessarily a problem. No ranking is a problem.”

Crosscheck Beall’s List

If the journal article doesn’t appear on the SJR, your predatory journal spidey sense should go off. Cross-reference the journal against Beall’s List, an archive of predatory journals created by librarian Jeffrey Beall. The sheer number of journals listed on Beall’s List is astounding, and it’s easy to see how naive readers could be duped.

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I’ve been feeling dejected since yesterday’s post. In fact, the whole research system has got me down. Let me be clear: I am not against research or science concept. What I am frustrated with, like everyone else, is the fucking system.

This all began when I came across something called predatory journals during my research for yesterday’s post. Predatory journals are junk journals at best and scams at worst. They exist because academics are under so much pressure to not only produce research but to publish research. You don’t get tenure without publishing. You don’t get a shot at tenure without publishing either. And given that the odds of landing a tenure-track job at a decent university are on par with making it to the NBA, young academics are under immense get work into the world.

Enter predatory journals.

To get a research article published in a competitive journal is an exercise in patience and will. I haven’t done it myself, but as the other half of a research academic trying to land a tenure track job, the process strikes me as the brainy equivalent of peeling your own skin off, letting it scab over, and peeling it off again. Some clever yet undeniably shitty people saw an opportunity to take advantage of struggling academics, so predatory journals were born. Think of them like the academic equivalent of late-night infomercials. For just $1700, you too can have your important research published in our official journal!

Yes, you read that right. Predatory journals often charge academics a fee for their work to appear in a bogus journal. That’s the hallmark of any good scam, right? They make you think you need them and then they take your money. It’s not always so obvious, though. Some journals are more sophisticated than others, leading well-intentioned researchers to unintentionally publish in hack journals. One study found that 5% of Italian researchers were duped into publishing in predatory journals.

In addition to exploiting academics, these journals don’t perform any sort of quality check on the work that gets submitted, leaving ample room for plagiarism, fraud, ethics, conflicts of interest, and general shitty science. But naive readers don’t know the difference, leading to a cancer of misinformation.

If this isn’t bad enough, predatory journals also create a sinkhole for funding and resources. One analysis found that 17% of articles sampled from predatory journals reported that their funding for the study was from the US Nationals Institute of Health (NIH). This means that one of the world’s foremost medical research center is funneling resources towards studies that end up in scientifically questionable journals.

“Little of this work will advance science,” the authors of the analysis say. “It is too dodgily reported (and possibly badly conducted) and too hard to find.”

What’s the deal with birds?

When I asked my partner, Justin, about predatory journals, he laughed and brushed it off with an, “I get spam emails from them all the time!” Then he giggled and pulled up a journal article entitled, “What’s the Deal with Birds?” published by Daniel T. Baldassarre in the Scientific Journal of Research and Reviews. The author, fed up with predatory journals, submitted a fancy-looking yet totally bogus “study” to a known predatory journal just to prove a point. And they actually published it, proving that just because something looks like research, doesn’t mean it’s legit.

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A note from Brooke: This post is taking longer than anticipated, so I’m splitting it into two parts. This post will focus on where to find relevant research papers, while Part II will focus on the quality and legitimacy of those articles.

For most of my life, I struggled with the assumption that people with letters after their name are not only smarter, more powerful, and more successful than me, but that the research they create is gospel. I’m not sure when or how this load-of-crap seed was planted, but it’s lead to a lifelong feeling of inadequacy—especially throughout my twenties. Doctors and scientists were busy saving lives and stumbling across eureka. Meanwhile, I made stupid cupcakes for a living and couldn’t afford health insurance.

My assumption that all doctors and research belonged on a pedestal is part of why I so easily accepted their mental health diagnosis. I knew I was depressed, but what did I know about how to fix it? A doctor told me that my brain was broken and that the pills I was taking did not have any major side effects. Who was I to question someone who spent 12 years learning how to identify and treat my exact problem?

It is only since getting off the antidepressants that I’ve begun to understand how complicated, political, and often corrupt the medical and research system actually is. And this isn’t conspiracy. Bad science is everywhere—The Guardian even has an entire vertical dedicated to it.

While researchers are adept at sorting out bad science from the good, regular folk rarely known the difference, which can lead to a plethora of misinformation and ill-informed opinion. But I’ve learned a few basic strategies to help us plebians suss out the good from bad. This is by no means foolproof, but it’s a start.

Where to find research papers

PubMed is a free search engine that primarily accesses the MEDLINE (Medical Literature Analysis and Retrieval System Online) database of research on life science and medical topics. It allows you to sort by a variety of matches, including author, publication date, and journal. It also has a nifty search feature that will only give you results that include free full text. Unfortunately, the full text of many research papers are hidden behind paywalls, which leaves the average person stuck with nothing but abstracts.

Google Scholar is…well, the Google of research. Whether you’re looking for research on antidepressants or conifer trees, Google Scholar is the grand poobah of scientific information. However, because Google Scholar is a search engine and not a subject-dedicated database (like PubMed), Google Scholar strives to include as many journals as possible, including junk journals and predatory journals. These predatory journals are known for exploiting the academic publishing business model, not checking journal articles for quality, and pushing agenda even in clear cases of fraudulent science.

All this to say that before a paper is read, the reader needs to do a bit of due diligence to make sure that what they’re reading is legitimate. Even then, we can’t be 100% sure. Case in point: Andrew Wakefield’s fraudulent research claiming that vaccines cause autism.

I know, I know. The number one rule in research is: don’t use Wikipedia as a source. Any old geezer (including you) can log on to Wikipedia and change an entry (any entry) to say anything and everything, which means that Wikipedia is riddled with errors and should not be referenced as truth in a research paper or reported article. But since we’re not reporting for the New York Times, Wikipedia is a good place to start because of the references listed at the bottom of each Wikipedia entry. The Wikipedia page on Antidepressant Discontinuation Syndrome, for example, links directly to 27 different sources on the topic. Whether or not all these references are legitimate is another issue entirely, and one that I will get into tomorrow when we explore Part II: How to tell if a journal article is legit.

As always, please keep in mind that like you, I am learning as I go. These are complicated topics that even experts don’t agree on. We’re all doing the best we can.

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Part of the reason why I’m able to learn what I’m learning is that my partner, Justin, is an academic. He’s built his career on reading, writing, and analyzing journal articles, which means he’s my first stop on the understanding research train. This is both great and terrible for me. On the one hand, I have an expert at my disposal. On the other hand, I have an expert at my disposal. What I think are straightforward questions turn into twenty-minute tirades that leave me more confused than before. No answer is ever simple, and I’ve been forced to accept that “it depends” is a valid conclusion.

“The more you research you read the more you’ll understand that every single study is fundamentally flawed,” he said to me yesterday. “Be careful about assumptions, because research studies are full of caveats and exceptions. They’re looking at one little sliver of one thing, and there’s no easy way to accurately translate that into something digestible and catchy for the media.”

All this because I asked him what n meant in a paper.

What is “n”?

I assumed the n operated like it does algebra, standing for a constant throughout the entire paper. As it turns out, that is entirely incorrect. There are big Ns and little ns. The big N typically stands for population size while the little stands for some sort of value. For example, if there are 1000 people in a school but only 200 of them were chosen for a study, N=1000 and n=200.

However, the n does not necessarily refer to human subjects and the meaning of that n can change with context. Using the paper from yesterday’s post as my example, we can see that there are a variety of values for n throughout different parts of the article. The first shows up in the abstract, n=16:

Reading the sentence before it, “antidepressants were significantly better than placebo in trials that had a low risk of bias,” this little n refers to the number of studies analyzed that had a low risk of bias (16 studies.) Why they can’t just say, “In the 16 trials that had a low risk of bias…” I don’t know.

Further down the paper, shows up again:

To understand what these ns represent, we need to read for context. The previous page states, “The literature searches from databases and additional resources identified 2890 relevant titles.” In this case, n has to do with the number of studies analyzed, and the chart breaks down how the researchers began with 2890 studies (2864 records identified through database searching + 26 records identified through other sources) and whittled their relevant studies down to the 28 included in the meta-analysis.

To sum up: An n is not an interpretation of the data but instead communicates some sort of numerical value. That value changes depending on what it’s referring to, so it’s always necessary to read for context.

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When I first began speaking openly about long term antidepressant use and antidepressant withdrawal, it didn’t take long for me to be faced with a wall of academic journals and research papers. At first, my instinct was to read the abstract, get the gist of what I was trying to understand and move on. But much like sourcing all your information exclusively from Fox News, that approach left me a dangerous kind of dumb. I had just enough information to confirm my bias but zero original thoughts surrounding the source, scope of work, journal reputation, limitations of the study, and industry response.

When it dawned on me that just reading the abstract was no better than just reading sensational news headlines and deeming yourself informed, I began to read the studies in full. At least, I tried. For those of us who haven’t spent their entire adult lives in research and academia, these papers are a nightmare.

While I understand that there are longstanding reasons why academic papers are written the way they’re written, it bothers me that only people with a PhD are taught to comprehend this sort of work. How can the individual be expected do their own research and make their own decisions for their own wellness if they can’t understand the research that policy and marketing is built upon?

Which brings me to the first installment of How to Read a Scientific Paper. I’m tired of taking other people’s word on research as gospel, so I’m going to learn how to do it myself and chronicle the journey here. Hopefully, I can beef up the entertainment factor, because damn these articles are dry.

I’m going to begin with a recent article spearheaded by psychiatrist Saeed Farooq and published in the Journal of Affective Disorders, entitled, “Pharmacological interventions for prevention of depression in high risk conditions: Systematic review and meta-analysis.”

I first found out about the study thanks to a Keele University tweet that said, “The study, led by Professor Saeed Farooq, found that using antidepressants as a pre-emptive measure could help to prevent depression in patients considered to be at high risk of developing the condition, for example following stroke or heart attack.” The tweet linked not to the article, but an in-house blog post that feels a bit too much like propaganda. The fact that we’re even considering doping people up on antidepressants before they become depressed deeply concerns me, so I want to learn more about it before I go full oh no you di’n’t! on the topic.

In reality, this was not a research study or clinical trial, but a systematic review and meta-analysis. And for us to learn to read journal articles, we must understand the difference.

What is a research article?

A research article is a study designed and performed by the paper’s author or authors. It will explain the methodology of the study—or rather, the methods and systems used to conduct the study—and clarify what the results mean. All of the steps are listed in detail in order to allow other researchers to conduct similar experiments.

One of the best ways to tell if you’re reading a research article is to look for phrases like “we found” or “I measured” or “we tested.” This indicated that the authors who are writing the article are the ones who also conducted the research.

Next, look at the formatting of the article. Research papers include sections that are listed in a particular order: abstract, introduction, methods, results, discussion, and references.

What is a review?

Review papers do not include original research conducted by the authors(s). Instead the author(s) give their thoughts on existing research papers for the purpose of identifying patterns or forming potential new conclusions based on a variety of research studies. For example, a researcher may look at a study performed in 1980 and compare it to a similar study from 2010 in order to provide an overview of the topic as a whole.

Reviews are particularly useful for people looking to get background information on a topic before diving into detailed or technical research papers. However, there is no formal process to dictate which articles must be included in a review, which gives authors the freedom to overlook existing research that may not fit their agenda. Thus, it can be difficult to determine if the author’s conclusions are biased.

What is a systematic review?

Systematic reviews were developed to eliminate that bias by requiring multiple authors to track down all available studies on a particular topic and execute high-level analysis of existing research in order to answer a clearly defined, clinical question. Systematic reviews can take months or years to complete, whereas standard reviews may only take a few weeks.

Systematic reviews contain a lot of data and to the untrained eye, can look a lot like original research. Systematic reviews are held in the same echelon as original research and are often presented to the public as if the research was new (like in the Keele University tweet.) This strikes me as potentially misleading, not because the research isn’t valid or useful, but because of the language used to promote the research.

For example, Farooq’s article concludes that based on his analysis, “Prevention of depression may be possible in patients who have high-risk conditions but the strategy requires complete risk and benefits analysis before it can be considered for clinical practice. However, not a single clinical study has been conducted to support or disprove that statement and the tweet says nothing about that and instead presents the research as if it were a new, exciting discovery.

What is meta-analysis?

Meta-analysis is a research process used to manage and interpret all the data for a systematic review. In layman’s terms, meta-analysis is how researchers make sense of the data in hundreds or thousands of individual papers. After extracting the data, analysts use a variety of methods to account for differences like sample size, variations in study approach that may affect the overall outcome of the systematic review, and overall findings.

Frankly, I don’t understand a lick of how meta-analysis works. But, I’ve learned that I don’t have to understand it as long as I understand what role it plays in research: meta-analysis pools the data sets from different studies into a single statistical set of data in order to analyze it and come to a single conclusion.

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For or those of you who like visuals, check out this article by Concordia University that visually breaks down the structure of various journal articles so you can recognize what you’re reading.

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