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USMLE – Designing The Ultimate Questions

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Shot courtesy crystaljingsr @ Flickr (Creative Commons Attribution, Non-Commercial License)


There are strategies that examiners can employ to frame questions that are designed to stump you on an exam such as the USMLE. Many of these strategies are listed out in the Kaplan Qbook and I’m sure this stuff will be familiar to many. My favorite techniques are the ‘multi-step’ and the ‘bait-and-switch’.

The Multi-Step

Drawing on principles of probability theory, examiners will often frame questions that require you to know multiple facts and concepts to get the answer right. As a crude example:

“This inherited disease exclusive to females is associated with acquired microcephaly and the medical management includes __________________.”

Such a question would be re-framed as a clinical scenario (an outpatient visit) with other relevant clinical data such as a pedigree chart. To get the answer right, you would need:

  1. Knowledge of how to interpret pedigree charts and identify that the disease manifests exclusively in females.
  2. Knowledge of Mendelian inheritance patterns of genetic diseases.
  3. Knowledge of conditions that might be associated with acquired microcephaly.
  4. Knowledge of medical management options for such patients.

Now taken individually, each of these steps – 1, 2, 3 and 4 – has a probability of 50% that you could get it right purely by random guessing. Combined together however, which is what is necessary to get the answer, the probability would be 50% * 50% * 50% * 50% = 6.25% [combined probability of independent events]. So now you know why they actually prefer multi-step questions over one or two-liners! 🙂 Notice that this doesn’t necessarily have anything to do with testing your intelligence as some might think. It’s just being able to recollect hard facts and then being able to put them together. They aren’t asking you to prove a math theorem or calculate the trajectory of a space satellite 😛 !

The Bait-and-Switch

Another strategy is to riddle the question with chock-full of irrelevant data. You could have paragraph after paragraph describing demographic characteristics, anthropometric data, and ‘bait’ data that’s planted there to persuade you to think along certain lines and as you grind yourself to ponder over these things you are suddenly presented with an entirely unrelated sentence at the very end, asking a completely unrelated question! Imagine being presented with the multi-step question above with one added fly in the ointment. As you finally finish the half-page length question, it ends with ‘<insert-similar-disease> is associated with the loss of this enzyme and/or body part: _______________’. Very tricky! Questions like these give flashbacks and dejavu of  days from 2nd year med school, when that patient with a neck lump begins by giving you his demographic and occupational history. As an inexperienced med student you immediately begin thinking: ‘hmmm..okay, could the lump be related to his occupation? …hmm…’. But wait! You haven’t even finished the physical exam yet, let alone the investigations. As medics progress along their careers they tend to phase out this kind of analysis in favor of more refined ‘heuristics’ as Harrison’s puts it. A senior medic will often wait to formulate opinions until the investigations are done and will focus on triaging problems and asking if management options are going to change them. The keyword here is ‘triage’. Just as a patient’s clinical information in a real office visit is filled with much irrelevant data, so too are many USMLE questions. That’s not to say that demographic data, etc. are irrelevant under all conditions. Certainly, an occupational history of being employed at an asbestos factory would be relevant in a case that looks like a respiratory disorder. If the case looks like a respiratory disorder, but the question mentions an occupational history of being employed as an office clerk, then this is less likely to be relevant to the case. Similarly if it’s a case that overwhelmingly looks like an acute abdomen, then a stray symptom of foot pain is less likely to be relevant. Get my point? That is why many recommend reading the last sentence or two of a USMLE question before reading the entire thing. It helps you establish what exactly is the main problem that needs to be addressed.

Hope readers have found the above discussion interesting :). Adios for now!

Copyright © Firas MR. All rights reserved.

Decision Tree Questions In Genetics And The USMLE

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Courtesy cayusa@flickr. (creative commons by-nc license)

Courtesy cayusa@flickr. (creative commons by-nc license)

Just a quick thought. It just occurred to me that some of the questions on the USMLE involving pedigree analysis in genetics, are actually typical decision tree questions. The probability that a certain individual, A, has a given disease (eg: Huntington’s disease) purely by random chance is simply the disease’s prevalence in the general population. But what if you considered the following questions:

  • How much genetic code do A and B share if they are third cousins?
  • If you suddenly knew that B has Huntington’s disease, what is the new probability for A?
  • What is the disease probability for A‘s children, given how much genetic code they share with B?

When I’d initially written about decision trees, it did not at all occur to me at the time how this stuff was so familiar to me already!

Apply a little Bayesian strategy to these questions and your mind is suddenly filled with all kinds of probability questions ripe for decision tree analysis:

  • If the genetic test I utilize to detect Huntington’s disease has a false-positive rate x and a false-negative rate y, now what is the probability for A?
  • If the pre-test likelihood is m and the post-test likelihood is n, now what is the probability for A?

I find it truly amazing how so many geneticists and genetic counselors accomplish such complex calculations using decision trees without even realizing it! Don’t you 🙂 ?

Copyright © Firas MR. All rights reserved.

Does Changing Your Anwer In The Exam Help?

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monty hall paradox

The Monty Hall Paradox

One of the 3 doors hides a car. The other two hide a goat each. In search of a new car, the player picks a door, say 1. The game host then opens one of the other doors, say 3, to reveal a goat and offers to let the player pick door 2 instead of door 1. Is there an advantage if the the player decides to switch? (Courtesy: Wikipedia)

Hola amigos! Yes, I’m back! It’s been eons and I’m sure many of you may have been wondering why I was MIA. Let’s just say it was academia as usual.

This post is unique as it’s probably the first where I’ve actually learned something from contributors and feedback. A very critical audience and pure awesome discussion. The main thrust was going to be an analysis of the question, “If you had to pick an answer in an MCQ randomly, does changing your answer alter the probabilities to success?” and it was my hope to use decision trees to attack the question. I first learned about decision trees and decision analysis in Dr. Harvey Motulsky’s great book, “Intuitive Biostatistics“. I do highly recommend his book. As I pondered over the question, I drew a decision tree that I extrapolated from his book. Thanks to initial feedback from BrownSandokan (my venerable computer scientist friend from yore :P) and Dr. Motulsky himself, who was so kind as to write back to just a random reader, it turned out that my diagram was wrong and so was the original analysis. The problem with the original tree (that I’m going to maintain for other readers to see and reflect on here) was that the tree in the book is specifically for a math (or rather logic) problem called the Monty Hall Paradox. You can read more about it here. As you can see, the Monty Hall Paradox is a special kind of unequal conditional probability problem, in which knowing something for sure, influences the probabilities of your guesstimates. It’s a very interesting problem, and has bewildered thousands of people, me included. When it was originally circulated in a popular magazine,  “nearly 1000 PhDs” (cf. Wikipedia) wrote back to say that the solution put forth was wrong, prompting numerous psychoanalytical studies to understand human behavior. A decision tree for such a problem is conceptually different from a decision tree for our question and so my original analysis was incorrect.

So what the heck are decision trees anyway? They are basically conceptual tools that help you make the right decisions given a couple of known probabilities. You draw a line to represent a decision, and explicitly label it with a corresponding probability. To find the final probability for a number of decisions (or lines) in sequence, you multiply or add their individual probabilities. It takes skill and a critical mind to build a correct tree, as I learned. But once you have a tree in front of you, its easier to see the whole picture.

Let’s just ignore decision trees completely for the moment and think in the usual sense. How good an idea is it to change an answer on an MCQ exam such as the USMLE? The Kaplan lecture notes will tell you that your chances of being correct are better off if you don’t. Let’s analyze this. If every question has 1 correct option and 4 incorrect options (the total number of options being 5), then any single try on a random choice gives you a probability of 20% for the correct choice and 80% for the incorrect choice. The odds are higher that on any given attempt, you’ll get the answer wrong. If your choice was correct the first time, it still doesn’t change these basic odds. You are still likely to pick the incorrect choice 80% of the time. Borrowing from the concept of “regression towards the mean” (repeated measurements of something, yield values closer to said thing’s mean), we can apply the same reasoning to this problem. Since the outcomes in question are categorical (binomial to be exact), the measure of central tendency used is the Mode (defined as the most commonly or frequently occurring thing in a series). In a categorical series – cat, dog, dog, dog, cat – the mode is ‘dog’. Since the Mode in this case happens to be the category “incorrect”, if you pick a random answer and repeat this multiple times, you are more likely to pick an incorrect answer! See, it all make sense 🙂 ! It’s not voodoo after all 😀 !

Coming back to decision analysis, just as there’s a way to prove the solution to the Monty Hall Paradox using decision trees, there’s also a way to prove our point on the MCQ problem using decision trees. While I study to polish my understanding of decision trees, building them for either of these problems will be a work in progress. And when I’ve figured it all out, I’ll put them up here. A decision tree for the Monty Hall Paradox can be accessed here.

To end this post, I’m going to complicate our main question a little bit and leave it out in the void. What if on your initial attempt you have no idea which of the answers is correct or incorrect but on your second attempt, your mind suddenly focuses on a structure flaw in one or more of the options? Assuming that an option with a structure flaw can’t be correct, wouldn’t this be akin to Monty showing the goat? One possible structure flaw, could be an option that doesn’t make grammatical sense when combined with the stem of the question. Does that mean you should switch? Leave your comments below!

Hope you’ve found this post interesting. Adios for now!

Copyright © Firas MR. All rights reserved.

Readability grades for this post:

Flesch reading ease score:  72.4
Automated readability index: 7.8
Flesch-Kincaid grade level: 7.3
Coleman-Liau index: 8.5
Gunning fog index: 11.4
SMOG index: 10.7


Intuitive Biostatistics, by Harvey Motulsky

The Monty Hall Problem: The Remarkable Story Of Math’s Most Contentious Brain Teaser, by Jason Rosenhouse

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USMLE Scores – Debunking Common Myths

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Lot’s of people have misguided notions as to the true nature of USMLE scores and what exactly they represent. In my opinion, this occurs in part due to a lack of interest in understanding the logistic considerations of the exam. Another contributing factor could be the bordering brainless, mentally zero-ed scientific culture most exam goers happen to be cultivated in. Many if not most of these candidates, in their naive wisdoms got into Medicine hoping to rid themselves of numerical burdens forever!

The following, I hope, will help debunk some of these common myths.

Percentile? Uh…what percentile?

This myth is without doubt, the king of all 🙂 . It isn’t uncommon that you find a candidate basking in the self-righteous glory of having scored a ’99 percent’ or worse, a ’99 percentile’. The USMLE at one point used to provide percentile scores. That stopped sometime in the mid to late ’90s. Why? Well, the USMLE organization believed that scores were being unduly given more weightage than they ought to in medics’ careers. This test is a licensure exam, period. That has always been the motto. Among other things, when residency programs started using the exam as a yard stick to differentiate and rank students, the USMLE saw this as contrary to its primary purpose and said enough is enough. To make such rankings difficult, the USMLE no longer provides percentile scores to exam takers.

The USMLE does have an extremely detailed FAQ on what the 2-digit (which people confuse as a percentage or percentile) and 3-digit scores mean. I strongly urge all test-takers to take a hard look at it and ponder about some of the stuff said therein.

Simply put, the way the exam is designed, it measures a candidate’s level of knowledge and provides a 3-digit score with an important import. This 3-digit score is an unfiltered indication of an individual’s USMLE know-how, that in theory shouldn’t be influenced by variations in the content of the exam, be it across space (another exam center and/or questions from a different content pool) or time (exam content from the future or past). This means that provided a person’s knowledge remains constant, he or she should in theory, achieve the same 3-digit score regardless of where and when he or she took the test. Or, supposedly so. The minimum 3-digit score that is required to ‘pass’ the exam is revised on an annual basis to preserve this space-time independent nature of the score. For the last couple of years, the passing score has hovered around 185. A ‘pass’ score makes you eligible to apply for a license.

What then is the 2-digit score? For god knows what reason, the Federation of State Medical Boards (these people provide medics in the US, licenses based on their USMLE scores) has a 2-digit format for a ‘pass’ score on the USMLE exam. Unlike the 3-digit score this passing score is fixed at 75 and isn’t revised every year.

How does one convert a 3-digit score to a 2-digit score? The exact conversion algorithm hasn’t been disclosed (among lots of other things). But for matters of simplicity, I’m going to use a very crude approach to illustrate:

Equate the passing 3-digit score to 75. So if the passing 3-digit score is 180, then 180 = 75. 185 = 80, 190 = 85 … and so on.

I’m sure the relationship isn’t linear as shown above. For one, by very definition, a 2-digit score ends at 99. 100 is a 3-digit number! So let’s see what happens with our example above:

190 = 85, 195 = 90, 199 = 99. We’ve reached the 2-digit limit at this point. Any score higher than 199 will also be equated to 99. It doesn’t matter if you scored a 240 or 260 on the 3 digit scale. You immediately fall under the 99 bracket along with the lesser folk!

These distortions and constraints make the 2-digit score an unjust system to rank test-takers and today, most residency programs use the 3-digit score to compare people. Because the 3-digit to 2-digit scale conversion changes every year, it makes sense to stick to the 3-digit scale which makes comparisons between old-timers and new-timers possible, besides the obvious advantage in helping comparisons between candidates who deal/dealt with different exam content.

Making Assumptions And Approximate Guesses

The USMLE does provide Means and Standard Deviations on students’ score cards. But these statistics don’t strictly apply to them because they are derived from different test populations. The score card specifically mentions that these statistics are “for recent” instances of the test.

Each instance of an exam is directed at a group of people which form its test population. Each population has its own characteristics such as whether or not it’s governed by Gaussian statistics, whether there is skew or kurtosis in its distribution, etc. The summary statistics such as the mean and standard deviation will also vary between different test populations. So unless you know the exact summary statistics and the nature of the distribution that describes the test population from which a candidate comes, you can’t possibly assign him/her a percentile rank. And because Joe and Jane can be from two entirely different test populations, percentiles in the end don’t carry much meaning. It’s that simple folks.

You could however make assumptions and arbitrary conclusions about percentile ranks though. Say for argument sake, all populations have a mean equal to 220 and a standard deviation equal to 20 and conform to Gaussian statistics. Then a 3-digit score of:

220 = 50th percentile

220 + 20 = 84th percentile

220 + 20 + 20 = 97th percentile

[Going back to our ’99 percentile’ myth and with the specific example we used, don’t you see how a score equal to 260 (with its 2-digit 99 equivalent) still doesn’t reach the 99 percentile? It’s amazing how severely people can delude themselves. A 99 percentile rank is no joke and I find it particularly fascinating to observe how hundreds of thousands of people ludicrously claim to have reached this magic rank with a 2-digit 99 score. I mean, doesn’t the sheer commonality hint that something in their thinking is off?]

This calculator makes it easy to calculate a percentile based on known Mean and Standard Deviations for Gaussian distributions. Just enter the values for Mean and Standard Deviation on the left, and in the ‘Probability’ field enter a percentile value in decimal form (97th percentile corresponds to 0.97 and so forth). Hit the ‘Compute x’ button and you will be given the corresponding value of ‘x’.

99th Percentile Ain’t Cake

Another point of note about a Gaussian distribution:

The distance from the 0th percentile to the 25th percentile is also equal to the distance between the 75th and 100th percentile. Let’s say this distance is x. The distance between the 25th percentile and the 50th percentile is also equal to the distance between the 50th percentile and the 75th percentile. Let’s say this distance is y.

It so happens that x>>>y. In a crude sense, this means that it is disproportionately tougher for you to score extreme values than to stay closer to the mean. Going from a 50th percentile baseline, scoring a 99th percentile is disproportionately tougher than scoring a 75th percentile. If you aim to score a 99 percentile, you’re gonna have to seriously sweat it out!

It’s the interval, stupid

Say there are infinite clones of you existent in this world and you’re all like the Borg. Each of you is mentally indistinguishable from the other – possessing ditto copies of USMLE knowhow. Say that each of you took the USMLE and then we plot the frequencies of these scores on a graph. We’re going to end up with a Gaussian curve depicting this sample of clones, with its own mean score and standard deviation. This process is called ‘parametric sampling’ and the distribution obtained is called a ‘sampling distribution’.

The idea behind what we just did is to determine the variation that we would expect in scores even if knowhow remained constant – either due to a flaw in the test or by random chance.

The standard deviation of a sampling distribution is also called ‘standard error’. As you’ll probably learn during your USMLE preparation, knowing the standard error helps calculate what are called ‘confidence intervals’.

A confidence interval for a given score can be calculated as follows (using the Z-statistic):-

True score = Measured score +/- 1.96 (standard error of measurement) … for 95% confidence

True score = Measured score +/- 2.58 (standard error of measurement) … for 99% confidence

For many recent tests, the standard error for the 3-digit scale has been 6 [Every score card quotes a certain SEM (Standard Error of Measurment) for the 3-digit scale]. This means that given a measured score of 240, we can be 95% certain that the true value of your performance lies between a low of 240 – 1.96 (6) and a high of 240 + 1.96 (6). Similarly we can say with 99% confidence that the true score lies between 240 – 2.58 (6) and 240 + 2.58 (6). These score intervals are probablistically flat when graphed – each true score value within the intervals calculated has an equal chance of being the right one.

What this means is that, when you compare two individuals and see their scores side by side, you ought to consider what’s going on with their respective confidence intervals. Do they overlap? Even a nanometer of overlapping between CIs makes the two, statistically speaking, indistinguishable, even if in reality there is a difference. As far as the test is concerned, when two CIs overlap, the test failed to detect any difference between these two individuals (some statisticians disagree. How to interpret statistical significance when two or more CIs overlap is still a matter of debate! I’ve used the view of the authors of the Kaplan lecture notes here). Capiche?

Beating competitors by intervals rather than pinpoint scores is a good idea to make sure you really did do better than them. The wider the distance separating two CIs, the larger is the difference between them.

There’s a special scenario that we need to think about here. What about the poor fellow who just missed the passing mark? For a passing mark of 180, what of the guy who scored, say 175? Given a standard error of 6, his 95% CI definitely does include 180 and there is no statistically significant (using a 5% margin of doubt) difference between him and another guy who scored just above 180. Yet this guy failed while the other passed! How do we account for this? I’ve been wondering about it and I think that perhaps, the pinpoint cutoffs for passing used by the USMLE exist as a matter of practicality. Using intervals to decide passing/failing results might be tedious, and maybe scientific endeavor ends at this point. Anyhow, I leave this question out in the void with the hope that it sparks discussions and clarifications.

If you care to give it a thought, the graphical subject-wise profile bands on the score card are actually confidence intervals (95%, 99% ?? I don’t know). This is why the score card clearly states that if any two subject-wise profile bands overlap, performance in these subjects should be deemed equal.

I hope you’ve found this post interesting if not useful. Please feel free to leave behind your valuable suggestions, corrections, remarks or comments. Anything 🙂 !

Readability grades for this post:

Kincaid: 8.8
ARI: 9.4
Coleman-Liau: 11.4
Flesch Index: 64.3/100 (plain English)
Fog Index: 12.0
Lix: 40.3 = school year 6
SMOG-Grading: 11.1

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Copyright © 2006 – 2008 Firas MR. All rights reserved.

How Examinations And Diagnostic Tests Are Similar

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“You are more than a score”, or so the saying goes. But how much of that comes out as an emotional outburst as opposed to objective and rational thinking? Let’s try to see why the above is totally true, scientifically speaking.

In medicine, we’ve learned a lot about diagnostic tests, right? In fact everything investigative in nature can be considered a diagnostic test. Be it a screening exam for cervical cancer, that blood test for glucose, an X-ray for a broken arm, or your palpating hand feeling for that enlarged liver. Heck, even an entire research study could be considered a diagnostic test. The ‘null hypothesis’ technique often used in analytical research studies is nothing more than a diagnostic test of sorts.

When considering the dynamics of a diagnostic test, a fundamental underlying principle is that we separate what is observed via the test from the actual truth. In the case of tangible phenomena like death, disease and disability, it is quite easy to distinguish the actual truth from what the test predicts. Because of this, you have terms like ‘false positives’, ‘false negatives’ and the like. A pregnancy test for example could be positive, but you could easily compare that prediction to the actual outcome (pregnancy vs. non-pregnancy) and say that this particular test has got such and such false positive rates. More or less, all tests have the following attributes in this regard:-

  1. Sensitivity
  2. Specificity
  3. Positive Predictive Value
  4. Negative Predictive Value
  5. Validity/Accuracy
  6. Reliability/Precision

We ought to think about examinations such as the USMLE, etc. in this manner as well. Why? Well, because they are investigations too! Think of them as X-rays to diagnose your intelligence or whatever, if that metaphor helps. And as a consequence, notions about false positives, false negatives and all of the other things on that list also apply to them. Being the abstract intangible thing intelligence is, it is impossible to know its true value. And because there’s no way to compare prediction versus truth, it is impossible to say for sure what the false positive or negative rates (or any item on that list) for an exam are. And that’s why, ‘you are more than a score’ ! Statistically speaking, examinations are just so lame !

Do send in your comments!

Readability grades for this post:

Kincaid: 9.3
ARI: 9.8
Coleman-Liau: 12.9
Flesch Index: 58.0/100
Fog Index: 13.2
Lix: 42.9 = school year 7
SMOG-Grading: 12.0

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Copyright © 2006 – 2008 Firas MR. All rights reserved.

When Treatments Kill

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Assorted Pharmaceuticals


It truly amazes me how soon writers block can set in. As you can probably see, my enterprise hasn’t exactly seen a lot of throughput. LOL 😀 . Okay enough of microprocessor terminology and let’s get on to something really cool 🙂 .

Doctors are quite peculiar in the fact that they strive to kill their own profession, at least indirectly. You could say the same for police officers, firefighters and their like. If there weren’t disease, crime or fire incidents, each of these groups would have achieved their missions and would have wiped out the very purpose of their existence. In our never ending struggle with disease, we are prone to treating people. EBM has taught us that that might not necessarily be a good thing. End-of-Life care and palliative medicine have totally transformed our thinking about the very definition of the word treatment. Treatments may very well be characterized by lack of interventions. For instance, CPR (Cardiopulmonary Resuscitation) no longer is viewed as something absolutely necessary. Through EBM, we’ve come to realize that the overall success rate of CPR is a meager ~15%.¹ To many of us that sounds surprizing, doesn’t it? We also now have clearer statistical evidence on which patient groups have better vs. worse success rates. Given these statistical insights, it is perfectly reasonable in certain instances for people to be given the choice of a DNR (Do Not Resuscitate) order in their treatment plans. The risks of broken ribs, fat embolism and other complications of CPR outweigh the benefits in such cases. Similarly, maintaining full nutrition may not be that good an idea, again if it’s not contrary to the specifics of a given case (eg. the patient’s choice, etc.) . It has been found that the mild ketosis during the starved state can very well induce a sense of comfort in painful end-of-life conditions.¹ So if the patient requests not to be tube-fed, you’re not only obligated to respect this request from an ethical standpoint but from a scientific perspective as well. The list of interventions that could be withdrawn in palliative care goes on, but I don’t really want to focus on that here. In most of these situations, the primary reason for not intervening isn’t because intervening is likely to accelerate death.

Nor is this post’s intention to bring to your attention, side-effects of medications/interventions that might eventually kill. No, we are talking about entirely different beasts here.

There are rare cases when the situation at hand isn’t palliative in nature or one that has a side-effect angle to it. A couple of unique instances actually wherein, the act of intervening itself will in fact worsen a patient’s condition and likely result in death. These go in line with the medical myths we discussed in my last post. Notice how these beasts baffle your instincts. So without further ado, some of these include²:

  1. Infantile Botulism – an infectious process – yet antibiotics worsen the case and are contraindicated.
  2. Hemolytic Uremic Syndrome due to Shigella – an infectious process – yet again, antibiotics worsen symptoms and are contraindicated.
  3. Thrombotic Thrombocytopenic Purpura – a situation where there’s a platelet decline – yet platelet transfusions are contraindicated.

Note that these aren’t the only ones, so do watch out for others! It’ll do you and the patient a lot good!


  1. Current Medical Diagnosis & Treatment, Chapter. Palliative Care and Pain Management by Michael W. Rabow, MD; Steven Z. Pantilat, MD
  2. Kaplan Medical, Lecture Notes for the USMLE Step 1

Copyright © 2006 – 2008 Firas MR. All rights reserved.

Written by Firas MR

March 17, 2008 at 10:18 pm

Pay Attention To Medical Myths!

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A bouncing ball captured with a stroboscopic flash at 25 images per second.

(A bouncing ball captured with a stroboscopic flash at 25 images per second: Source)

Hola Amigos!

It indeed has been a very long time since my last entry. This time around, let’s focus on something different. I’d like to talk about medical myths and how they might wreak havoc in your lives. No, I don’t mean the food fads, the herbal remedies your grandma made popular and all that. This article’s meant for the doc in training.

Consider this question:-

What antibody response is typical of Extrinsic Allergic Alveolitis?

In case you hadn’t read about this disorder’s immunology specifically, your survival instincts would have immediately led you to say IgE. Given the utterly memory-oriented way our brain functions in modern medical study, it immediately homes onto specific keywords in the question. Allergy and IgE go hand in hand don’t they? Well, not in this case they don’t. The correct answer here happens to be IgG.¹ Examinations such as the USMLE will test you with these odd-balls and boy do they constitute a bunch! Regardless of the ethical issues concerning what should or shouldn’t be tested in medical examinations, knowing the right responses to such questions and indexing them to one’s already burdened memory can indeed make or break one’s career.

There’s another oddity that you’d be interested to know about Extrinsic Allergic Alveolitis. This is one lung disorder that has a lower incidence rate among smokers!²

That’s it for now. Hope you’ve found this post interesting. Your comments are welcome as always. Until my next post, Ciao!


  1. Current Pulmonary, Chapter 32. Hypersensitivity Pneumonitis, Clinical Findings; by Cecile Rose MD MPH
  2. Current Pulmonary, Chapter 32. Hypersensitivity Pneumonitis, Pathogenesis; by Cecile Rose MD MPH

——“Millions saw the apple fall, but Newton was the one who ASKED WHY.”
~ Bernard Mannes Baruch

Copyright © 2006 – 2008 Firas MR. All rights reserved.

Written by Firas MR

March 9, 2008 at 5:16 pm