Posts Tagged ‘USMLE’
USMLE – Designing The Ultimate Questions
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 ‘multistep’ and the ‘baitandswitch’.
The MultiStep
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 reframed 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:
 Knowledge of how to interpret pedigree charts and identify that the disease manifests exclusively in females.
 Knowledge of Mendelian inheritance patterns of genetic diseases.
 Knowledge of conditions that might be associated with acquired microcephaly.
 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 multistep questions over one or twoliners! :) 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 :P !
The BaitandSwitch
Another strategy is to riddle the question with chockfull 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 multistep question above with one added fly in the ointment. As you finally finish the halfpage length question, it ends with ‘<insertsimilardisease> 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!
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Copyright © Firas MR. All rights reserved.
Decision Tree Questions In Genetics And The USMLE
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 falsepositive rate x and a falsenegative rate y, now what is the probability for A?
 If the pretest likelihood is m and the posttest 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?
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 :D !
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
FleschKincaid grade level: 7.3
ColemanLiau index: 8.5
Gunning fog index: 11.4
SMOG index: 10.7
Intuitive Biostatistics, by Harvey Motulsky
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USMLE Scores – Debunking Common Myths
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 zeroed 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 selfrighteous 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 2digit (which people confuse as a percentage or percentile) and 3digit scores mean. I strongly urge all testtakers 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 3digit score with an important import. This 3digit score is an unfiltered indication of an individual’s USMLE knowhow, 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 3digit score regardless of where and when he or she took the test. Or, supposedly so. The minimum 3digit score that is required to ‘pass’ the exam is revised on an annual basis to preserve this spacetime 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 2digit 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 2digit format for a ‘pass’ score on the USMLE exam. Unlike the 3digit score this passing score is fixed at 75 and isn’t revised every year.
How does one convert a 3digit score to a 2digit 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 3digit score to 75. So if the passing 3digit 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 2digit score ends at 99. 100 is a 3digit number! So let’s see what happens with our example above:
190 = 85, 195 = 90, 199 = 99. We’ve reached the 2digit 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 2digit score an unjust system to rank testtakers and today, most residency programs use the 3digit score to compare people. Because the 3digit to 2digit scale conversion changes every year, it makes sense to stick to the 3digit scale which makes comparisons between oldtimers and newtimers 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 3digit 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 2digit 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 2digit 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 Zstatistic):
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 3digit scale has been 6 [Every score card quotes a certain SEM (Standard Error of Measurment) for the 3digit 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 subjectwise 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 subjectwise 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 :) !
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Readability grades for this post:
Kincaid: 8.8
ARI: 9.4
ColemanLiau: 11.4
Flesch Index: 64.3/100 (plain English)
Fog Index: 12.0
Lix: 40.3 = school year 6
SMOGGrading: 11.1
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Copyright © 2006 – 2008 Firas MR. All rights reserved.
Calling For A Common Worldwide Medical Licensure Pathway
Medicine – Realm Of The Unknown
For ages, the medical sphere has been shrouded in mystery – for people outside of medicine that is. And this hasn’t been too good for the medical profession because many policy makers on matters of healthcare/medicine aren’t sufficiently acquainted with its many nuances to yield considered judgements. Sometimes you just can’t help get the feeling that doctors have a language of their own, with a community so tightly knit that it borders some sort of illuminati like cult.
Earlier, most of this mystery was limited to the knowledge base of medicine. Doctors were treated like gods walking on earth and people had no qualms whatsoever in having blind faith in them. With the rapid rise of web technologies however, doctors find themselves facing tough and pointed questions by their patients and policy makers about the decisions they make.
Some aspects, for the large part, still remain hidden away however. Stuff that affects policy decisions and how medical communities across the world interact with each other. Issues concerning licensure and taxonomy immediately come to mind.
An aspect of medicine that to this day, remains an enigma for many ‘outsiders’ is the entire academic hierarchy that applies to medical systems across the globe. Many ‘insiders’ end up at their wits ends too. The taxonomy is definitely confusing. What the heck is a Senior Registrar? Or for that matter, what in god’s name is the difference between house surgeons/officers, resident medical officers, civil surgeons, residents, interns, attendings, senior house officers and all that jargon? The world could definitely use a universal taxonomic architecture for medical systems akin to the WHO’s International Classification of Diseases (ICD) to streamline stuff and make interactions between communities easier.
Licensure – One Too Many Exams For A Globalised Age
When medical students step into the medical world, being relatively new ‘insiders’ at this stage, very few are cognizant of the fact that their careers depend on having to satisfy licensure requirements before even thinking about pursuing higher education. Getting through medical school is one step. After that, students are required to go through long winded licensure pathways before even beginning to gain higher training. Licensure serves as a quality control measure to ensure the safety of patients and is arguably, a necessary evil.
Modern society depends on the exchange of ideas and talent between countries. The same applies to medicine as well. Unfortunately, due to the myriads of medical licensure exams across different countries, this kind of exchange and collaboration can become extremely tedious and at times impractical. Getting into higher training for the international trainee becomes a daunting task. Take the following hypothetical scenario:
Dr. Underdog went to medical school in a country bordering Angola and got his local medical license after graduating and passing local licensure exams. He now intends to gain higher training in colorectal surgery (… of all things :) ) in the US. Before getting into a higher training program he needs an American license. He proceeds to sit for the United States Medical Licensure Exam (USMLE) and passes all 4 component exams in this process with flying colors. Good for him, Dr. Underdog’s thirst for knowledge is relentless. After gaining qualifications as a colorectal surgeon, he is now interested in learning a highly advanced and experimental procedure involving cosmic radiation and bizarre tumor polyps :P , only available in Australia. He is now required to pass the Australian Medical Council licensure exams before he begins. He goes ahead with that and gains the skills he’s always dreamed about :) . By now, Dr. Underdog has been through at least a dozen different licensure exams. The exams he gave in the US and Australia weren’t directly related to the subjects he studied at those places. Seeing great potential in this emerging pioneer, a group of people from a country near Chile invite Dr. Underdog over. They’d like him to impart some of the training he received to a couple of their fortunate students. Unfortunately, he needs to clear their local licensure exams before he can begin. He candidly goes through that as well. In this new land, Dr. Underdog meets a fellow international doc who’s been through twice the number of licensure exams as he has, to get to a position as senior faculty member while also dealing with some mind blowing research – literally involving blowing stuff :P , partly as an outlet for his bottled up frustrations over licensure systems. … See how tedious it can get?
If I’m interested in gaining specialized skills and/or knowledge available in only certain parts of the world, I need to get straight down to business without having to worry about sitting for multiple licensure exams. Sitting for multiple licensure exams is not only wasteful of time and money, it is also redundant. Most of these exams test the same content anyway. Most importantly, as an aspiring international trainee, my focus has to be on the exams directly related to the training I intend to pursue rather than random licensure tests.
Solution? A universal licensure pathway ratified by an international body such as the WHO that should be acceptable to all countries.
At the moment, a few agencies such the Medical Council of Canada and the Australian Medical Council are conducting joint licensure tests. Their efforts in this direction are laudable and should be wholeheartedly welcomed. Hopefully other countries will follow suit and some day a universal licensure pathway will become a reality. Until then, international trainees can only follow in Dr. Underdog’s tortuous footsteps!
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Readability grades for this post:
Kincaid: 10.0
ARI: 11.2
ColemanLiau: 14.4
Flesch Index: 53.2/100
Fog Index: 13.1
Lix: 48.9 = school year 9
SMOGGrading: 12.0
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Copyright © 2006 – 2008 Firas MR. All rights reserved.
International Medical Graduates and the NRMP 200708
UPDATE, 2nd April 2008: This post is now openaccess.
Now that the 2007–08 US Residency Match is over, let’s review some interesting statistics. Preliminary/Transitional programs will not be reviewed in this post. All programs covered are Categorical.
2008 NRMP Match data imply data gathered from the 2007–08 Match session for residency programs beginning in July 2008. FYI, programs and seats are not equivalent. Any given program will typically have >1 seat.
For your reading pleasure, this article has been framed in a Q&A format . Let’s begin right away:
 What specialty do I stand a reasonable chance at?
 Based on the number of unfilled programs (number in brackets indicates number of unfilled programs as opposed to the total number of unfilled individual seats),
 Family Medicine (105)
 Pediatrics (40)
 Internal Medicine (31)
 Pathology (27)
 Psychiatry (27)
 Emergency Medicine (11)
 The above specialties also have more seats than the number of US seniors applying for them.
 Based on the number of unfilled programs (number in brackets indicates number of unfilled programs as opposed to the total number of unfilled individual seats),
 How is this relevant?
 Well, typically if there’s a (>=) 1:1 US senior:Seat ratio, that means in order for you to secure a position you will need to displace a US senior. That can happen if the program views you as a superior enough candidate to justify hiring you over a US senior. And this is not all hunky dory for the average IMG Joe.
 What about General Surgery?
 For a total of 1069 seats, the number of US seniors competing was 1161. That gives us a US senior:Seat ratio = 1.1 . Therefore, seats in Surgery are far fewer than the number of US senior applicants competing for them, let alone nonUSsenior applicants, and finding a position is going to be difficult as per the aforementioned Firas’s Law of ‘Displacement’ . Only 2 seats went unfilled for 2008.
 How many applicants competed for Internal Medicine in comparison?
 The US senior:Seat ratio was 0.6 . Not only that, a significant number of programs went entirely unfilled by any group.
 Has there been a renewed interest in any of the above ‘high yield’ specialties?
 Emergency Medicine saw an increase in the number of positions being offered, by 10% between 2004 and 2008. The number of US seniors filling EM positions has also increased by the same number during this period.
 Has interest in US residency training or competition increased or decreased?
 Increased. Overall, there has been a 13.4% increase in the number of Active Applicants (meaning applicants who did not withdraw their applications for some reason or the other) between 2004 and 2008. The increase in Active IMG Applicants (both US citizen and nonUS citizen) has been even greater than this number during the same period. This could possibly be due to shrinking opportunities for quality training in other parts of the world.
 Is Internal Medicine really that disliked by US seniors?
 Match data indicate that IM is still where more USseniors end up than in any other specialty.
 Is an average, runofthemill nonUS citizen IMG more likely to succeed than not?
 No. Although this will depend on the specific specialty in question. In general, by random chance alone, a nonUS citizen IMG is more likely to be unsuccessful. Not only that; success rates have dropped from previous years. The match success rate for NonUS citizen IMGs and UScitizen IMGs for 2008 were 42.4% and 51.9% respectively.
That end’s my wrapup for the NRMP 2008 data. For more indepth coverage, the NRMP stats are available on NRMP’s website. Another great resource is Charting Outcomes in the Match: Characteristics of Applicants who Matched to Their Preferred Specialty in the 2007 NRMP Main Residency Match published by the AAMC available for free on its website.
Please feel free to leave behind your comments! They aren’t gonna cost ya anything !
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When Treatments Kill
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 :D . 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. EndofLife 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 endoflife conditions.¹ So if the patient requests not to be tubefed, 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, sideeffects 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 sideeffect 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:
 Infantile Botulism – an infectious process – yet antibiotics worsen the case and are contraindicated.
 Hemolytic Uremic Syndrome due to Shigella – an infectious process – yet again, antibiotics worsen symptoms and are contraindicated.
 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!
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References:
 Current Medical Diagnosis & Treatment, Chapter. Palliative Care and Pain Management by Michael W. Rabow, MD; Steven Z. Pantilat, MD
 Kaplan Medical, Lecture Notes for the USMLE Step 1

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