The Egg Study and elephant in the room

The Egg Study published in the Journal of the American Medical Association this month has been highly publicised and criticised among evidence-based thinkers. As it has been adequately questioned as proof that eggs increase risk of cardiovascular disease, criticism has missed the elephant in the room: it was a negative study! 

An elephant in the room is missed when our focus is shifted to a less important issue. In this case, the criticism has been mistakenly concentrated on the observational nature of the study. In this post, I will first explain why criticism is out of focus and second I will reveal the elephant in the room, explaining why it is a negative rather than a positive study. 

Observational Study for Harm

In the first half of last century, 80% of western population were smokers and it was not considered harmful. Gastroenterologist Richard Doll investigated smoking as a possible cause for peptic ulcer and found no association. Then, he looked beyond his specialty and investigated lung cancer, in collaboration with famous statistician Austin Bradford Hill. This investigation led to the landmark article published in the British Medical Journal in 1950 demonstrating beyond a reasonable doubt that smoking leads to lung cancer. It was an observational study. And so far, of course, there is no randomised clinical trial to cigaret smoking versus placebo smoking to prove this causal relationship. 

Would we criticise the ideia that smoking cause cancer because it came from an observational study? So why do we criticise the observational nature of the Egg Study for testing the ideia that eggs cause cardiovascular disease. 

This criticism misses the difference between testing harm and testing beneficial effects, which relies on the burden of proof. In testing harm, a positive study will lead to the recommendation of “avoid” or “not to do”. In testing beneficial effect, a positive result will lead to the recommendation of “to do”. The negative consequence of an inappropriate recommendation of “to do” tends to be worse than a recommendation of “avoid”.

It is appropriate to criticise recommendations to eat or to take a medicine based on observational studies. Hormonal replacement therapy was recommended for cardiovascular prevention based on observational data and later randomised data indicated this therapy increases cardiovascular events. Also, so many dietary myths has been created by observational data.

On the other hand, in testing for harm, observational studies should not be considered inadequate as a rule of thumb. If two conditions are satisfied, well-designed observational studies might be taken as evidence for causation: first, a high biological plausibility, leading to high pre-test probability of the hypothesis; second, a very strong association: the hazard ratio for smoking and cancer or for alcohol and hepatic cirrhosis are both around 20, meaning a 1900% relative risk increase. 

The hypothesis tested in the Egg Study was one of harm. So, instead of criticising the nature of the study, we must read it carefully in search for these two conditions.  

Regarding pre-test probability of this hypothesis, it is difficult to comprehend how half an egg per day would be enough to increase risk of cardiovascular events, since eggs are just an small portion of dietary cholesterol, which is a weak determinant of plasma cholesterol. Second, the Egg Study shows a very weak association not fulfilling our condition for causation: hazard ratio = 1.06, just a 6% relative increase.

Therefore, this observational study should not be considered confirmatory in the sense that egg consumption is a risk factor for cardiovascular disease. 

The Elephant in the Room

Along with egg consumption, the study evaluated total dietary cholesterol. The analysis of the direct effect of eggs and total dietary cholesterol, adjusted to each other, differentiates between the causal or non-causal nature of the relationship between eggs and cardiovascular events.

See how the analysis tells a history that makes sense: 

Both eggs and total dietary cholesterol were associated with incident cardiovascular events during a median follow-up of 17.5 years. Each additional 300 mg of dietary cholesterol per day increased the hazard by 17% after adjustment for risk factors. Each additional half an egg per day would increase a tine 6% of hazard after adjustment for risk factors. 

Now the multivariate analysis: when eggs were adjusted for total dietary cholesterol, eggs totally lost statistical significance. It suggests egg consumption is just a marker of a diet rich in cholesterol. To confirm this thought, when total dietary cholesterol were adjusted for eggs, its hazard ratio remained the same, equally significant. Thus, eggs are not a major determinant of total dietary cholesterol in this sample. 

The first analysis makes the study negative for independent prediction value of eggs to cardiovascular events. The second analysis shows that the independent predictor is total dietary cholesterol, regardless of eggs. 

In addition with that, there is another trick in differentiating causation and confounding: to compare specific to non-specific mortality. 

Mortality depends on a chain of events subjected to confounding. So the analysis of cause-specific mortality provides insight by comparing the different natures of deaths. 

An effective way to differentiate causation and confounding is to test the association between the preditor and an “outside outcome”. Cardiovascular mortality is an “inside outcome” on the hypothesis that egg causes cardiovascular disease. Non-cardiovascular mortality has nothing to do with this hypothesis, being an “outside outcome”, which can be related with the same confounding as the “inside outcome” does. If the candidate risk factor is equally associated with the inside (cardiovascular mortality) and the outside outcome (non-cardiovascular mortality), the association has little to do with causation. The same confounding are mediating the two associations. 

In this study, eggs consumption is associated with cardiovascular mortality. It may make sense. But it was similarly associated with non-cardiovascular mortality, which does not make sense. It indicates a strong influence of confounding in this epidemiological ecosystem. 

These two interpretations make the Egg Study strongly negative for the hypothesis that eggs cause cardiovascular disease. 

I admit it is harder to read an observational study in comparison with a randomised clinical trial. In observational studies, interpretation of results should take into consideration the multivariate analysis, which contains clues of the true reality. 

My Diet

I eat one egg per day, at breakfast. The average consumption in US is half per day. If the association demonstrated in the study were causal, my egg habit would increase my risk by 6%. As a 49 year old male, with no risk factors, I have 5% risk of cardiovascular events. Eating my egg at breakfast would increase risk from 5% to 5,3%. Therefore, I’d keep my eggs even if it was a randomised clinical trial.

It makes me think. Normally, we first analyse if the evidence is true. Then, we ask if it is relevant. Maybe we should invert this order. We should ask first if the association makes a difference. If not, we should not care if it is true.

The disaster of Brumadinho was a black swan?

Nassim Taleb's concept of the black swan defines (1) rare, (2) unpredictable and (3) highly impactful events. It is black swans that dictate the course of humanity: the crash of 1929, Hitler's insurgency, discovery of antibiotics, iPhone, internet, September 11. None of these rare events could be predicted, prevented or planned. The more unpredictable, the more impactful.

In science, the logic of the black swan makes scientists aware of the role of serendipity and chance for great discoveries. True scientists rely less on plausible theories, focus on experimentation, and recognise unusual results when they arise. They respect the black swan.

In the interpretation of social or clinical history, the lack of recognition of the concept of the black swan causes us to interpret events based on fallacious causal hypotheses, created by the phenomenon of "retrospective predictability": we tell the story backwards, inventing a meaning for the fact.

On the other hand, our mental elaboration must realize when the event is not a black swan. Thus, the differentiation between white and black swans must be at the heart of society's scientific literacy.

Three years ago, the worst environmental disaster in Brazil took place.  A dam belonging to mining company Vale collapsed, killing 19 people and destroying the city of Mariana.

At that time, I thought: was it a black swan? Although analysts blamed Vale, I wondered if the impression that such a catastrophe was preventable would be a narrative fallacy. That was a rare and highly impactful event. As no one predicted, it could be an unpredictable event. The three criteria of the black swan would be present.

Last week another such event took place in the same state of Minas Gerais, caused by a collapsed dam from the same company. It destroyed the region of Brumadinho and is supposed to have killed hundreds. 

Brumadinho unraveled the dilemma: the collapse of the Vale dams are not black swans. When the same event occurs in a short period, it is no longer rare and unpredictable. Two casual events probably do not repeat themselves in such a short period of time. Scientifically, reproducibility reduces the likelihood of chance.

The perception that this was not by chance implies the possibility of prevention from the identification of causes. 

However, a concern arises ...

Since the event lost its unusual characteristic, its potential impact in preventable attitudes may have decreased. According to the logic of the black swan, impact is proportional to how unusual the event is. Brumadinho was no longer unpredictable. Thus, after the trauma passes and the news are naturally diluted, the likelihood of a government behavioural change towards preventable mode may be lower than after Mariana's unusual disaster.

We should be aware: Brumadinho is not a black swan!

The Parachute Trial: useful caricature or just a joke?

Caricature studies" have been used successfully in the scientific field to make relevant methodological discussions more palatable. I like this approach and often use them as teaching tools, such as the strong correlation between chocolate consumption and Nobel Prizes as an example of confounding bias.

In 2003, a systematic review on efficacy of parachute use in patients who jumped from great heights was published in the British Medical Journal. The review indicated no randomized clinical trials for this intervention. It was a clever way of demonstrating that not everything needs experimental evidence. That article inspired us to create the terms "parachute paradigm" and "principle of extreme plausibility".

Yesterday, I received a plethora of enthusiastic messages about the latest clinical trial published in the British Medical Journal as part of the Christmas series: Parachute use to prevent death and major trauma when jumping from aircraft: randomized controlled trial.

In this trial, airplane passengers were invited to enter a study where they would jump from the plane to the ground, after being randomized to the use of parachute or non-parachute backpack as a control group. The primary outcome was death or severe trauma. Based on the premise that 99% of the control group would suffer the outcome, for a 99% power to detect a huge (and plausible) relative risk reduction of 95%, only 10 patients per group would be needed. This was done and, surprisingly, the study was negative: zero incidence of the primary outcome in both groups. However, only individuals who would jump from planes parked on the ground agreed to participate in the work.

Funny, but what is the implicit message of this study?

"Randomized trials might selectively enroll individuals with a lower perceived likelihood of benefit, thus diminishing the applicability of the results to clinical practice."

According to the authors, the new parachute study would be pointing to the problem that randomized clinical trials select samples less predisposed to the benefit of the intervention, a phenomenon that would promote false negative studies. The authors explain that it happens because patients who are more likely to benefit from therapy are less likely to agree to enter a study in which they may be randomized to non-treatment. This would make clinical trial samples less sensitive to benefit detection as a partial exclusion of patients with a greater chance of therapeutic success would take place.

Caricatures serve to accentuate true traits. However, if we were to characterize clinical trial samples (ideal world), they tend to be more predisposed to finding positive results in comparison with a real world target population. Therefore, this study is not a caricature of the real world clinical trial.

Thus, the present article should lose the caricature status and be considered just a funny joke, with no ability to anchor our mind towards a better scientific thinking.

As proof of concepts, clinical trials rely on the use of highly treatment-friendly samples, by applying restrictive inclusion and exclusion criteria. Differences between patients who accept and do not agree to enter the study are not sufficient to generate a sample less predisposed to treatment benefit than reality.

The "joke study" commits an unusual sample bias: it allows the inclusion of patients who do not need treatment. It would be as if a study aimed at testing thrombolysis allowed the inclusion of any chest pain, regardless of the electrocardiogram. Doctors who already believe in thrombolysis would see the electrocardiogram, thrombolyze ST-elevation patients, and release those who do not need thrombolysis to be randomized to drug or placebo. A joke without scientific value.

Caricature studies are useful when they anchor the mind of the community to a sharper criticism of the results of studies. However, in this case, the anchoring occurred in the opposite direction.

First, when we think of the scientific ecosystem, the biggest problem is false positive studies, mediated by several phenomena: confounding bias in observational studies, outcome reporting bias, conclusions skewed to positive finding  (spin) and, finally, citation bias that favor positive studies. Behind all this lies the innate predilection of the human mind for false statements, to the detriment of true denials.

Secondly, there is the problem of efficacy (ideal world) versus effectiveness (real world). Clinical trials aim to evaluate efficacy, which could be interpreted as the intrinsic potential of the intervention to offer clinical benefit: "Does the treatment have beneficial ownership?" Therefore clinical trials represent the ideal condition for the treatment to work. In the face of a positive clinical trial, we must always reflect whether this positivity will be reproduced in the real world, which constitutes effectiveness.

Of course there is the problem of false negative studies and it should also be a concern. But the bias suggested by the funny parachute study does not represent an important false-negative mechanism. The most prevalent mechanisms leading to false negatives are reduced statistical power, excessive crossover in the intention-to-treat analysis and inadequate intervention applicability.

My concern is that a reader of this funny study would take the following message home: if a promising study is negative, consider that clinical trials tend to include patients less likely to the benefit from the intervention. This message is wrong, as clinical trials tend to select samples more predisposed to the benefit. Of course, there are exceptions, but if we are to anchor our mind, it should be in the direction of the most prevalent phenomenon.

My prediction is that this study will come to be cited by legions of believers not satisfied with negative results from well-designed studies. Just as the seminal article of the parachute has been used inadequately as a justification for many treatments that have nothing to do with the parachute paradigm under the premise that "there is no evidence at all." A recent study by Vinay Prasad has shown that most interventions characterized as parachute paradigm by medical articles are not that, many have had clinical trials with negative results.

The great attention received by the parachute clinical trial is an example of how information sharing on social networks occurs. The main criterion for sharing is the interesting, unusual or amusing character, to the detriment of the veracity or usefulness of the information. In the appeal for novelty, fake news end up getting more attention than true news, as was recently demonstrated by a paper published in Science. Although the article we are discussing should not be framed as fake news, it is not a good caricature of the real world either.

The work in question is not a caricature of the ecosystem of randomized clinical trials. It is a mere joke with the potential to bias our minds to the inadequate idea that the heterogeneity between clinical trial samples and the target population of the treatment reduces the sensitivity of these studies to detect positive effects. In fact, the samples enrolled in clinical trials usually have a greater chance to detect positive results (sensitivity) than if the entire target population were included.

When the learning of science is approached in a fun way, it arouses great interest of the biomedical community. But we should always ask ourselves: what is the implicit message of the caricature? It is the first step to the critical appraisal of such “thought experiments”.

The Bright Side of “Many Analysis, One Data Set” Paper

An elegant paper led by English researchers and recently published in the journal Advances in Methods and Practices in Psychological Science has enhanced scientific skepticism regarding ascertainment of statistical data analyses. Using exactly the same database, 29 independent research groups provided a priori data analysis plan to test the hypothesis that referees tend to give red cards more often to dark-skin-toned soccer players in comparison with light-skin-toned players. The analysis performed by 20 groups statistically confirmed the hypothesis, while 9 groups had non-significant statistical analysis.

Amidst of the scientific concern hype ignited by this paper, I have to confess that this time my interpretation leaned towards optimism. Considering the complexity of the problem analyzed, the observational nature of the data and the large variability of statistical methods chosen by the researchers, I found the results presented by different groups surprising similar. 

The authors described that odds ratio of dark-skin-toned players for getting red cards, in relation to light-skin-toned players, varied between 0.89 and 2.93. Although this interval appears to suggest high variation of results, by looking carefully at the forest plot depicted in the figure below, it becomes clear that most studies have similar odds ratios and confidence intervals. Actually, there were two outliers with odds ratio of 2.88 and 2.93 and extremely large confidence intervals. Something in those statistical analysis made these two studies very imprecise. On the other hand, the rest of studies had quite similar results.

Considering all 29 studies, we calculated an average odds ratio of 1.39, with 95% confidence interval between 1.22 and 1.55. If we exclude the two outliers, the average odds ratio is 1.28 (95% CI = 1.21 - 1.33, very precise). In reality, agreement among studies regarding both point estimate odds ratios and confidence intervals is quite good.

Furthermore, while 20 studies demonstrated a positive association between the dark-skin-toned players and odds to get a red card, no studies suggested the opposite result. The remaining 9 analysis basically did not reject the null hypothesis.

Assuming the true result is the one presented by most studies, none of the 9 discordant studies had made the most serious random error of claiming falsity (type I error). All 9 studies would have made the type II error, that is, they simply failed to reject the null hypothesis. Considering the association being explored is not strong (odds ratio < 2), it is only natural that some of the analyses lacked sufficient statistical power.

The problem presented to the researchers was quite complex. The observational nature of the data leads to potential confounding, along with concerns regarding independence of observations. Statistical analysis had to address heterogeneities between players according to skin-tone, referees predisposition to give red cards, relationship among players and referees, different soccer leagues, among other things. 

I may comply with a “half empty glass” interpretation of the study: choices for statistical approaches for complex epidemiological data vary substantially and this variation leads to a certain level of   disagreement among studies. On the other hand, I am more inclined to a “half full glass” interpretation: for a very complex problem, odds ratio estimation was surprisingly reproducible, most studies rejected the null hypothesis in the same direction and no studies suggested the opposite result. Moreover, if we take into consideration less complex statistical circumstances, such as the case for a typical well-designed large randomized controlled trial, the prospect may be quite good.

The Magical Transformation of a Secondary into a Primary Outcome

The reading of a scientific study should involve a domain beyond the scientific article, encompassing the ecosystem that involves the creation of the idea, definition of the protocol and acceptance of the results by the community. The reading of the work does not begin, nor does it end in the final article.

In a recent post, we provoked the reflection about the uncertain result of the SCOT-HEART clinical trial. That analysis was solely based on my reading of the article published in the NEJM. In the present article I will go further, beyond the final publication. 

In the journal club of my cardiology department, we use a peculiar methodology to read articles. One of these aspects is the orientation for our resident to systematically access clinicaltrial.gov and look for inconsistencies between the protocol defined a priori and what is in the published article. We are constantly evaluating the ecosystem prior to the article.

That was when our chief resident, Dr. João Menezes, came up with another surprise about the SCOT-HEART trial: the primary outcome reported in the NEJM publication was actually one of many secondary outcomes, exemplifying "the magical transformation of a secondary into a primary outcome".

The transformation

The scientific integrity of a study depends on a priori definition of data analysis. This method serves to avoid the multiplicity of tests that would increase the probability of type I error. In this context, it is essential to define the primary outcome of the study, which should guide the conclusion, instead of relying on the positive secondary outcomes results that may suffer from the multiple comparison problem.

The publication of SCOT-HEART in the NEJM clearly states "The primary endpoint was death from coronary heart disease or nonfatal myocardial infarction at 5 years."

"Our pre-specified primary long-term endpoint was the proportion of patients who died from coronary heart disease or had a nonfatal myocardial infarction at 5 years."

Let us now go to the ecosystem prior to the article. As we know, authors should record the protocol of any clinical trial prior to its execution and this is usually done at clinicaltrials.gov.

Upon checking the study protocol on clinicaltrials.gov, João realized that the primary outcome described in NEJM was not a true primary outcome! As in a magic spell, a prior secondary outcome was made primary in the description of the final article.

In fact, this study was originally designed to evaluate the proportion of patients who received a diagnosis of coronary disease, comparing tomography versus control strategy. This proportion was the primary outcome pre-defined by the study.

The secondary outcomes were divided into 5 domains (symptoms, diagnosis, additional investigations, treatment implemented, long-term clinical outcomes). In the domain of clinical outcomes, 9 secondary endpoints have been described, among which is the "cardiovascular death and nonfatal infarction" end-point, now described as primary in the NEJM article.

Look at the description of the secondary clinical outcomes as set out in clinicaltrials.gov and the Trials article describing the study design in 2012:

1. Cardiovascular death or non-fatal Myocardial Infarction (MI) (ii) Cardiovascular death (iii) Non-fatal MI (iv) Cardiovascular death, non-fatal MI or non-fatal stroke (v) Non-fatal stroke (vi) All-cause death (vii) Coronary revascularisation; percutaneous coronary intervention or coronary artery bypass graft surgery (viii) Hospitalisation for chest pain including acute coronary syndromes and non-coronary chest pain (ix) Hospitalisation for cardiovascular disease including coronary artery disease, cerebrovascular disease and peripheral arterial disease.

To complicate matters further, clinical outcomes were pre-defined to be evaluated at 10-year follow-up and the article describes a 5-year follow-up. Thus, 5 years follow-up was not a priori definition. Strictly speaking, we are faced with a secondary outcome defined a posteriori (post-hoc analysis). This is not just semantics, because in the absence of a definition of when the outcome should be evaluated, we can test it year by year, hoping that chance presents us with a positive result at some point. At the moment the author is gifted by chance, he can prepare an abstract and submit to an important international congress. I'm not saying that's how it was done, I'm just showing what can be done with post-hoc endpoints.

In this way, we are facing a serious problem of multiple comparisons, which can be computed as follows:

Considering the 5% alpha, if the null hypothesis is true (tomography group = control group), the probability of a false positive result in a single primary outcome is 5%. However, we are making 9 secondary attempts to get a positive result. If each of these attempts has a 5% probability of a false positive result, the probability of a false positive result appearing in any of the outcomes is 1 - 0.95^K, where K is the number of trials. Thus, the likelihood of any of these secondary outcomes being false-positive is 36%. Much higher than the 5% if we were analyzing a single primary outcome.

To aggravate, the statistical power of SCOT-HEART, after correction for the actual incidence of the outcome, is only 27%, as we mentioned in previous post. We then have two mechanisms of randomly manufacturing a false-positive: multiple endpoints tested and a study that lacks statistical power. In this way, the probability of false-positive becomes greater than 36%. Third, if we consider the risk of outcome bias (ascertained by electronic medical records, not adjudicated), SCOT-HEART is a random and systematic machine for generating false results.

This is one more explanation for the unlikely relative hazard reduction of 41% in the incidence of the combined outcome of infarction and cardiovascular death at 5 years of follow-up after coronary CT. As discussed in the previous article, the prevention of a clinical outcome by conducting an examination depends on three conditional probabilities (abnormal finding P x change in treatment P x ​​beneficial effect the changing treatment), different from the probability of benefit of a treatment that has only one component. To aggravate the gradient of changing treatment between the groups was only 4%.

In this way, it is too good to be true that conducting an examination promotes a benefit with the usual magnitude of good treatments, what usually ranges from 20% to 40%. Here we refer to the relative reduction because it describes the intrinsic "effect size" of a treatment, which does not vary with absolute risk. For example, the relative risk reduction of death for heart failure by enalapril or bata-blocker is 16% and 35%, respectively.

Two Scandals

It is scandalous for the authors to describe as primary outcome an outcome that was pre-defined as secondary. This shows the lack of scientific integrity behind the scenes of this work.

Perhaps more shocking is the acceptance of this article by the medical community, which seemed to commemorate the outcome of the work, featured prominently at the European Congress of Cardiology in Munich.

Problems of scientific integrity do not belong to a morally defective individual. Lack of scientific integrity stems from a faulty ecosystem, through research producers, editors and reviewers, and those who read the article without the necessary critical insight.

The Legion Bias

We might think it is very strange that thousands of cardiologists simultaneously attending the presentation of the study in ESC congress agreed with the doubtful result. Should the number of enthusiastic people be an evidence in favor of study truthfulness?

It is worth reminiscing to the observation of Swedish physician and statistician Hans Rosling, who became famous for his TED lectures, using dynamic statistical graphs to show how most people are wrong about important facts of life.

Rosling used to ask such questions to a legion of intellectuals: "How many children from low-income countries have basic education? 20%, 40%, or 60%?" The correct answer is 60%, but only 7% of intellectuals responded correctly. Most people chose 20%. Note that if we asked a monkey what the correct alternative would be, it would hit 33% of the time. Why do sapiens hit just 7%? The answer lies in our extreme bias. We tend to believe in the most significant result (most positive), whether we are talking about a risk factor or the beneficial effect of a treatment. Our mind has a tropism towards the highest possible contrast, making us choose the most extreme result.

It is a collective phenomenon, creating a legion of believers in the most significant result. The immense number of people thinking the same way, reinforces the belief of the legion participants. It's the legion bias.

The problem worsens when we are medical specialists, enthusiastic about our technological tools. This justifies the belief medical community deposited in small and biased studies of hypothermia post cardiac arrest and beta-blockers in non-cardiac surgery, which have become recommendations in guideline; or hormone replacement therapy from observational studies. The same is true for SCOT-HEART, which, when presented with glamor at the European Society of Cardiology Meeting, created its own legion of believers.

The Novelty, Positivism and Confirmation Biases

SCOT-HEART is the most recent study, so it appears as a novelty that promotes knowledge evolution. However, there was already another study published years earlier. This is the PROMISE study: a larger study (10,000 patients), a truly primary outcome defined a priori, with follow-up for evaluation of outcomes, adjudicated. That is, PROMISE is an immensely superior quality study to SCOT-HEART. And its result was negative.

Why, then, do we prefer to believe in the positive evidence of poor quality than in the negative evidence of good quality? Because our mind has a tropism for the positive (bias of positivism) and for the new (novelty bias). Then, we use the confirmation bias (we select positive evidence and disregard negative ones) to reinforce our belief.

By considering the cognitive biases of the biological mind, we do not need to be rude mentioning possible conflicts of interest that can also move the legions of believers.

The King Who Was Naked

It tells the story of Hans Christian Andersen (1937) that a very vain king ordered two tailors an unprecedented outfit, so original that no one had ever dressed the same. In the impossibility of realizing the king's desire, the tailors devised an imaginary costume, which they claimed to be invisible to the eyes of stupid people. The king himself, when he tried on his clothes, could not see it in the mirror, but pretended to see in order not to look stupid. In the same way, all people realized that the king was naked, but no one drew his attention for fear of being considered stupid. And so the king spent much of his reign naked, exposed to ridicule. The fear of looking stupid made people accept the unbelievable. In fact, many believed that they were seeing the clothes, because they wanted to believe that they were not stupid.

This story portrays the mechanism by which some myths persist in medicine.

One fine day, during an important parade in a public square, when a child saw the king passing by, he cried out: the king is naked! This child unmasked the charade created by the tailors, embarrassed the king, and especially the subjects who believed the lie or were ashamed to disagree.

Some interpret that it was the innocence of the child that allowed its observation. In fact, he was one of those half-malicious children. In this case, the difference between child and adult was the courage to acknowledge the truth and disagree with the legion of fanatics.

Let SCOT-HEART alert to the multiple biases that keep us from scientific integrity.