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Posts Tagged ‘Medical Science

Revitalizing Science Education

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Richard Feynman: “… But you’ve gotta stop and think about it. About the complexity to really get the pleasure. And it’s all really there … the inconceivable nature of nature! …”

And when I read Feynman’s description of a rose — in which he explained how he could experience the fragrance and beauty of the flower as fully as anyone, but how his knowledge of physics enriched the experience enormously because he could also take in the wonder and magnificence of the underlying molecular, atomic, and subatomic processes — I was hooked for good. I wanted what Feynman described: to assess life and to experience the universe on all possible levels, not just those that happened to be accessible to our frail human senses. The search for the deepest understanding of the cosmos became my lifeblood [...] Progress can be slow. Promising ideas, more often than not, lead nowhere. That’s the nature of scientific research. Yet, even during periods of minimal progress, I’ve found that the effort spent puzzling and calculating has only made me feel a closer connection to the cosmos. I’ve found that you can come to know the universe not only by resolving its mysteries, but also by immersing yourself within them. Answers are great. Answers confirmed by experiment are greater still. But even answers that are ultimately proven wrong represent the result of a deep engagement with the cosmos — an engagement that sheds intense illumination on the questions, and hence on the universe itself. Even when the rock associated with a particular scientific exploration happens to roll back to square one, we nevertheless learn something and our experience of the cosmos is enriched.

Brian Greene, in The Fabric of The Cosmos

When people think of “science education”, they usually tend to think about it in the context of high school or college. When in reality it should be thought of as encompassing education life-long, for if analyzed deeply, we all realize that we never cease to educate ourselves no matter what our trade. Because we understand that what life demands of us is the capacity to function efficiently in a complex society. As we gain or lose knowledge, our capacities keep fluctuating and we always desire and often strive for them to be right at the very top along that graph.

When it comes to shaping attitudes towards science, which is what I’m concerned about in this post, I’ve noticed that this begins quite strongly during high school, but as students get to college and then university, it gradually begins to fade away, even in some of the more scientific career paths. By then I guess, some of these things are assumed (at times you could say, wrongfully). We aren’t reminded of it as frequently and it melts into the background as we begin coping with the vagaries of grad life. By the time we are out of university, for a lot of us, the home projects, high-school science fests, etc. that we did in the past as a means to understand scientific attitude, ultimately become a fuzzy, distant dream.

I’ve observed this phenomenon as a student in my own life. As med students, we are seldom reminded by professors of what it is that constitutes scientific endeavor or ethic. Can you recall when was the last time you had didactic discussions on the topic?

I came to realize this vacuum early on in med school. And a lot of times this status quo doesn’t do well for us. Take Evidence-Based-Medicine (EBM) for example. One of the reasons, why people make errors in interpreting and applying EBM in my humble opinion, is precisely because of the naivete that such a vacuum allows to fester. What ultimately happens is that students remain weak in EBM principles, go on to become professors, can not teach EBM to the extent that they ought to and a vicious cycle ensues whereby the full impact of man’s progress in Medicine will not be fulfilled. And the same applies to how individuals, departments and institutions implement auditing, quality-assurance, etc. as well.

A random post that I recently came across in the blogosphere touched upon the interesting idea that when you really think about it, most practicing physicians are ultimately technicians whose job it is to fix and maintain patients (like how a mechanic oils and fixes cars). The writer starts out with a provocative beginning,

Is There A Doctor In The House?


Medical doctors often like to characterize themselves as scientists, and many others in the public are happy to join them in this.

I submit, however, that such a characterization is an error.


and divides science professionals into,


SCIENTIST: One whose inquiries are directed toward the discovery of new facts.

ENGINEER: One whose inquiries are directed toward the new applications of established facts.

TECHNICIAN: One whose inquiries are directed toward the maintenance of established facts.


and then segues into why even if that’s the case, being a technician in the end has profound value.

Regardless of where you find yourselves in that spectrum within this paradigm, I think it’s obvious that gaining skills in one area helps you perform better in others. So as technicians, I’m sure that practicing physicians will find that their appraisal and implementation of EBM will improve if they delve into how discoverers work and learn about the pitfalls of their trade. The same could be said of learning about how inventors translate this knowledge from the bench to the bedside as new therapies, etc. are developed and the caveats involved in the process.

Yet it is precisely in these aspects that I find that medical education requires urgent reform. Somehow, as if by magic, we are expected to do the work of a technician and to get a grip on EBM practices without a solid foundation for how discoverers and inventors work.

I think it’s about time that we re-kindled the spirit of understanding scientific attitude at our higher educational institutions and in our lives (for those of us who are already out of university).

From self-study and introspection, here are a couple of points and questions that I’ve made a note of so far, as I strive to re-invigorate the scientific spirit within me, in my own way. As you reflect on them, I hope that they are useful to you in working to become a better science professional as well:

  1. Understand the three types of science professionals and their roles. Ask where in the spectrum you lie. What can you learn about the work professionals in the other categories do to improve how you yourself function?
  2. Learning about how discoverers work, helps us in getting an idea about the pitfalls of science. Ultimately, questions are far more profound than the answers we keep coming up with. Do we actually know the answer to a question? Or is it more correct to say that we think we know the answer? What we think we know, changes all the time. And this is perfectly acceptable, as long as you’re engaged as a discoverer.
  3. What are the caveats of using language such as the phrase “laws of nature”? Are they “laws”, really? Or abstractions of even deeper rules and/or non-rules that we cannot yet touch?
  4. Doesn’t the language we use influence how we think?
  5. Will we ever know if we have finally moved beyond abstractions to deeper rules and/or non-rules? Abstractions keep shifting, sometimes in diametrically opposite directions (eg: from Newton’s concepts of absolute space-time to Einstein’s concepts of relative space-time, the quirky and nutty ideas of quantum mechanics such as the dual nature of matter and the uncertainty principle, concepts of disease causation progressing from the four humours to microbes and DNA and ultimately a multifactorial model for etiopathogenesis). Is it a bad idea to pursue abstractions in your career? Just look at String Theorists; they have been doing this for a long time!
  6. Develop humility in approach and knowledge. Despite all the grand claims we make about our scientific “progress”, we’re just a tiny speck amongst the billions and billions of specks in the universe and limited by our senses and the biology of which we are made. The centuries old debate among philosophers of whether man can ever claim to one day have found the “ultimate truth” still rages on. However, recently we think we know from Kurt Godel’s work that there are truths out there in nature that man can never arrive at by scientific proof. In other words, truths that we may never ever know of! Our understanding of the universe and its things keeps shifting continuously, evolving as we ourselves as a species improve (or regress, depending on your point of view). Understanding that all of this is how science works is paramount. And there’s nothing wrong with that. It’s just the way it is! :-)
  7. Understand the overwhelming bureaucracy in science these days. But don’t get side-tracked! It’s far too big of a boatload to handle on one’s own! There are dangers that lead people to leave science altogether because of this ton of bureaucracy.
  8. Science for career’s sake is how many people get into it. Getting a paper out can be a good career move. But it’s far more fun and interesting to do science for science’s own sake, and the satisfaction you get by roaming free, untamed, and out there to do your own thing will be ever more lasting.
  9. Understand the peer-review process in science and its benefits and short-comings.
  10. Realize the extremely high failure rate in terms of the results you obtain. Over 90% by most anecdotal accounts – be that in terms of experimental results or publications. But it’s important to inculcate curiosity and to keep the propensity to question alive. To discover. And to have fun in the process. In short, the right attitude; despite knowing that you’re probably never going to earn a Fields medal or Nobel prize! Scientists like Carl Friederich Gauss were known to dislike publishing innumerable papers, favoring quality over quantity. Quite contrary to the trends that Citation Metrics seem to have a hand in driving these days. It might be perfectly reasonable to not get published sometimes. Look at the lawyer-mathematician, Pierre de Fermat of Fermat’s Last Theorem fame. He kept notes and wrote letters but rarely if ever published in journals. And he never did publish the proof of Fermat’s Last Theorem, claiming that it was too large to fit in the margins of a copy of a book he was reading as the thought occurred to him. He procrastinated until he passed away, when it became one of the most profound math mysteries ever to be tackled, only to be solved about 358 years later by Andrew Wiles. But the important thing to realize is that Fermat loved what he did, and did not judge himself by how many gazillion papers he could or could not have had to his name.
  11. Getting published does have a sensible purpose though. The general principle is that the more peer-review the better. But what form this peer-review takes does not necessarily have to be in the form of hundreds of thousands of journal papers. There’s freedom in how you go about getting it, if you get creative. And yes, sometimes, peer-review fails to serve its purpose. Due to egos and politics. The famous mathematician, Evariste Galois was so fed-up by it that he chose to publish a lot of his work privately. And the rest, as they say, is history.
  12. Making rigorous strides depends crucially on a solid grounding in Math, Probability and Logic. What are the pitfalls of hypothesis testing? What is randomness and what does it mean? When do we know that something is truly random as opposed to pseudo-random? If we conclude that something is truly random, how can we ever be sure of it? What can we learn from how randomness is interpreted in inflationary cosmology in the manner that there’s “jitter” over quantum distances but that it begins to fade over larger ones (cf. Inhomogeneities in Space)? Are there caveats involved when you create models or conceptions about things based on one or the other definitions of randomness? How important is mathematics to biology and vice versa? There’s value in gaining these skills for biologists. Check out this great paper1 and my own posts here and here. Also see the following lecture that stresses on the importance of teaching probability concepts for today’s world and its problems:


  13. Developing collaborative skills helps. Lateral reading, attending seminars and discussions at various departments can help spark new ideas and perspectives. In Surely You’re Joking Mr. Feynman!, the famous scientist mentions how he always loved to dabble in other fields, attending random conferences, even once working on ribosomes! It was the pleasure of finding things out that mattered! :-)
  14. Reading habits are particularly important in this respect. Diversify what you read. Focus on the science rather than the dreary politics of science. It’s a lot more fun! Learn the value of learning-by-self and taking interest in new things.
  15. Like it or not, it’s true that unchecked capitalism can ruin balanced lives, often rewarding workaholic self-destructive behavior. Learning to diversify interests helps take off the pressure and keeps you grounded in reality and connected to the majestic nature of the stuff that’s out there to explore.
  16. The rush that comes from all of this exploration has the potential to lead to unethical behavior. It’s important to not lose sight of the sanctity of life and the sanctity of our surroundings. Remember all the gory examples that  WW2 gave rise to (from the Nazi doctors to all of those scientists whose work ultimately gave way to the loss of life that we’ve come to remember in the euphemism, “Hiroshima and Nagasaki”). Here’s where diversifying interests also helps. Think how a nuclear scientist’s perspectives could change about his work if he spent time taking a little interest in wildlife and the environment. Also, check this.
  17. As you diversify, try seeing science in everything – eg: When you think about photography think not just about the art, but about the nature of the stuff you’re shooting, the wonders of the human eye and the consequences of the arrangement of rods and cones and the consequences of the eyeball being round, its tonal range compared to spectral cameras, the math of perspective, and the math of symmetry, etc.
  18. Just like setting photography assignments helps to ignite the creative spark in you, set projects and goals in every avenue that you diversify into. There’s no hurry. Take it one step at a time. And enjoy the process of discovery!
  19. How we study the scientific process/method should be integral to the way people should think about education. A good analogy although a sad one is, conservation and how biology is taught at schools. Very few teachers and schools will go out of their way to emphasize and interweave solutions for sustainable living and conserving wildlife within the matter that they talk about even though they will more than easily get into the nitty-gritty of the taxonomy, the morphology, etc. You’ll find paragraphs and paragraphs of verbiage on the latter but not the former. This isn’t the model to replicate IMHO! There has to be a balance. We should be constantly reminded about what constitutes proper scientific ethic in our education, and it should not get to the point that it begins to fade away into the background.
  20. The current corporate-driven, public-interest research model is a mixed bag. Science shouldn’t in essence be something for the privileged or be monopolized in the hands of a few. Good ideas have the potential to get dropped if they don’t make business sense. Understand public and private funding models and their respective benefits and shortcomings. In the end realize that there are so many scientific questions out there to explore, that there’s enough to fill everybody’s plate! It’s not going to be the end of the world, if your ideas or projects don’t receive the kind of funding you desire. It’s ultimately pretty arbitrary :-) ! Find creative solutions to modify your project or set more achievable goals. The other danger in monetizing scientific progress is the potential to inculcate the attitude of science for money. Doing science for the joy of it is much more satisfying than the doing it for material gain IMHO. But different people have different preferences. It’s striking a balance that counts.
  21. The business model of science leads us into this whole concept of patent wars and Intellectual Property issues. IMHO there’s much value in having a free-culture attitude to knowledge, such as the open-access and open-source movements. Imagine what the world would be like if Gandhi (see top-right) patented the Satyagrah, requiring random licensing fees or other forms of bondage! :-)
  22. It’s important to pursue science projects and conduct fairs and workshops even at the university level (just as much as it is emphasized in high school; I would say to an even greater degree actually). Just to keep the process of discovery and scientific spirit vibrant and alive, if for no other reason. Also, the more these activities reflect the inter-relationship between the three categories of science professionals and their work, the better. Institutions should recognize the need to encourage these activities for curricular credit, even if that means cutting down on other academic burdens. IMHO, on balance, the small sacrifice is worth it.
  23. Peer-review mechanisms currently reward originality. But at the same time, I think it’s important to reward repeatability/reproducibility. And to reward statistically insignificant findings. This not only helps remove bias in published research, but also helps keep the science community motivated in the face of a high failure rate in experiments, etc.
  24. Students should learn the art of benchmarking progress on a smaller scale, i.e. in the experiments, projects, etc. that they do. In the grand scheme of things however, we should realize that we may never be able to see humongous shifts in how we are doing in our lifetimes! :-)

    Srinivasa Ramanujan

    Srinivasa Ramanujan

  25. A lot of stuff that happens at Ivy League universities can be classified as voodoo and marketing. So it’s important to not fret if you can’t get into your dream university. The ability to learn lies within and if appropriately tapped and channelized can be used to accomplish great stuff regardless of where you end up studying. People who graduate from Ivy League institutes form a wide spectrum, with a surprising number who could easily be regarded as brain-dead. IMHO what can be achieved is a lot more dependent on the person rather than the institution he or she goes to. If there’s a will, there’s a way! :-) Remember some of science’s most famous stalwarts like Michael Faraday and Srinivasa Ramanujan were largely self-taught!
  26. Understand the value of computing in science. Not only has this aspect been neglected at institutes (especially in Biology and Medicine), but it’s soon getting indispensable because of the volume of data that one has to sift and process these days. I’ve recently written about bioinformatics and computer programming here and here.
  27. It’s important to develop a level of honesty and integrity that can withstand the onward thrust of cargo-cult science.
  28. Learn to choose wisely who your mentors are. Factor in student-friendliness, the time they can spend with you, and what motivates them to pursue science.
  29. I usually find myself repelled by demagoguery. But if you must, choose wisely who your scientific heroes are. Are they friendly to other beings and the environment? You’d be surprised as to how many evil scientists there can be out there! :-)

I’m sure there are many many points that I have missed and questions that I’ve left untouched. I’ll stop here though and add new stuff as and when it occurs to me later. Send me your comments, corrections and feedback and I’ll put them up here!

I have academic commitments headed my way and will be cutting down on my blogular activity for a while. But don’t worry, not for long! :-)

I’d like to end now, by quoting one of my favorite photographers, George Steinmetz:

George Steinmetz: “… I find that there is always more to explore, to question and, ultimately, to understand …”


  1. Bialek, W., & Botstein, D. (2004). Introductory Science and Mathematics Education for 21st-Century Biologists. Science, 303(5659), 788-790. doi:10.1126/science.1095480

Copyright Firas MR. All Rights Reserved.

“A mote of dust, suspended in a sunbeam.”

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Written by Firas MR

November 6, 2010 at 5:21 am

Let’s Face It, We Are Numskulls At Math!

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Noted mathematician, Timothy Gowers, talks about the importance of math

I’ve often written about Mathematics before Footnotes. As much as math helps us better understand our world (Modern Medicine’s recent strides have a lot to do with applied math for example), it also tells us how severely limited man’s common thinking is.

Humans and yes some animals too, are born with or soon develop an innate ability for understanding numbers. Yet, just like animals, our proficiency with numbers seems to stop short of the stuff that goes beyond our immediate activities of daily living (ADL) and survival. Because we are a higher form of being (or allegedly so, depending on your point of view), our ADLs are a lot more sophisticated than say those of, canaries or hamsters. And consequently you can expect to see a little more refined arithmetic being used by us. But fundamentally, we share this important trait – of being able to work with numbers from an early stage. A man who has a family with kids knows almost by instinct that if he has two kids to look after, that would mean breakfast, lunch and dinner times 2 in terms of putting food on the table. He would have to buy two sets of clothes for his kids. A kid soon learns that he has two parents. And so on. It’s almost natural. And when someone can’t figure out their way doing simple counting or arithmetic, we know that something might be wrong. In Medicine, we have a term for this. It’s called acalculia and often indicates the presence of a neuropsychiatric disorder.

It’s easy for ‘normal’ people to do 2 + 2 in their heads. Two oranges AND two oranges make a TOTAL of four oranges. This basic stuff helps us get by day-to-day. But how many people can wrap their heads around 1 divided by 0? If you went to school, yea sure your teachers must have hammered the answer into you: infinity. But how do you visualize it? Yes, I know it’s possible. But it takes unusual work. I think you can see my point, even with this simple example. We haven’t even begun to speak about probability, wave functions, symmetries, infinite kinds of infinities, multiple-space-dimensions, time’s arrow, quantum mechanics, the Higgs field or any of that stuff yet!

As a species, it is so obvious that we aren’t at all good at math. It’s almost as if we construct our views of the universe through this tunneled vision that helps us in our day-to-day tasks, but fails otherwise.

We tend to think of using math as an ability when really it should be thought of as a sensory organ. Something that is as vital to understanding our surroundings as our eyes, ears, noses, tongues and skins. And despite lacking this sense, we tend to go about living as though we somehow understand everything. That we are aware of what it is to be aware of. This can often lead to trouble down the road. I’ve talked about numerous PhDs having failed at the Monty Hall Paradox before. But a recent talk I watched, touched upon something with serious consequences that meant people being wrongfully convicted because of a stunted interpretation of DNA, fingerprint evidence, etc. by none other than “expert” witnesses. In other words, serious life and death issues. So much for our expertise as a species, eh?!

How the human mind struggles with math!

We recently also learned that the hullabaloo over H1N1 pandemic influenza had a lot do with our naive understanding of math, the pitfalls of corporate-driven public-interest research notwithstanding.

Anyhow, one of my main feelings is that honing one’s math not only helps us survive better, but it can also teach us about our place in the universe. Because we can then begin to fully use it as a sensory organ in its own right. Which is why a lot of pure scientists have argued that doing math for math’s own sake can not only be great fun (if done the right way, of course :-P) but should also be considered necessary. Due to the fact that such research has the potential to reveal entirely new vistas that can enchant us and surprise us at the same time (take Cantor’s work on infinity for example). For in the end, discovery, really, is far more enthralling than invention.

UPDATE 1: Check out the Khan Academy for a virtually A-Z education on math — and all of it for free! This is especially a great resource for those of us who can’t even recall principles of addition, subtraction, etc. let alone calculus or any of the more advanced stuff.

Copyright © Firas MR. All rights reserved.

# Footnotes:

  1. ذرا غور فرمائیے اپنے انسان ہونے کی حیثیت پر
  2. Decision Tree Questions In Genetics And The USMLE
  3. The Story Of Sine
  4. On The Impact Of Thinking Visually
  5. A Brief Tour Of The Field Of Bioinformatics
  6. Know Thy Numbers!

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The Mucking About That Pervades Academia In Scientific Pursuit

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Bureaucracy (by Kongharald @ Flickr by-sa license)

Howdy readers!

I’ve not had the chance yet to delve into the bureaucracy of academia in science, having relegated it to future reading and followup. Some interesting reading material that I’ve put on my to-read list for future review is:

Academic medicine: a guide for clinicians
By Robert B. Taylor

Advice for a Young Investigator
By Santiago Ramón y Cajal, Neely Swanson, Larry W. Swanson

Do let me know if there any others that you’ve found worth a look.

In the meantime, I just caught the following incisive read on the topic via a trackback to my blog from a generous reader:

    Lawrence, P. A. (2009). Real Lives and White Lies in the Funding of Scientific Research. PLoS Biol, 7(9), e1000197. doi:10.1371/journal.pbio.1000197

Writing about the odious tentacles that young academics have to maneuver against, author Peter Lawrence of Cambridge (UK) says that “the granting system turns young scientists into bureaucrats and then betrays them”.

He then goes on to describe in detail with testimonies from scientists as to how and why exactly that’s the case. And concludes that not only does the status quo fundamentally perverse freedom in scientific pursuit but also causes unnecessary wastage sometimes to the detriment of people’s careers and livelihoods despite their best endeavors to stay dedicated to the pursuit of scientific knowledge. And how this often leads to die hard researchers dropping out from continuing research altogether!

Some noteworthy excerpts (Creative Commons Attribution License):


The problem is, over and over again, that many very creative young people, who have demonstrated their creativity, can’t figure out what the system wants of them—which hoops should they jump through? By the time many young people figure out the system, they are so much a part of it, so obsessed with keeping their grants, that their imagination and instincts have been so muted (or corrupted) that their best work is already behind them. This is made much worse by the US system in which assistant professors in medical schools will soon have to raise their own salaries. Who would dare to pursue risky ideas under these circumstances? Who could dare change their research field, ever?—Ted Cox, Edwin Grant Conklin Professor of Biology, Director of the Program on Biophysics, Princeton University


the present funding system in science eats its own seed corn [2]. To expect a young scientist to recruit and train students and postdocs as well as producing and publishing new and original work within two years (in order to fuel the next grant application) is preposterous. It is neither right nor sensible to ask scientists to become astrologists and predict precisely the path their research will follow—and then to judge them on how persuasively they can put over this fiction. It takes far too long to write a grant because the requirements are so complex and demanding. Applications have become so detailed and so technical that trying to select the best proposals has become a dark art. For postdoctoral fellowships, there are so many arcane and restrictive rules that applicants frequently find themselves to be of the wrong nationality, in the wrong lab, too young, or too old. Young scientists who make the career mistake of concentrating on their research may easily miss the deadline for the only grant they might have won.


After more than 40 years of full-time research in developmental biology and genetics, I wrote my first grant and showed it to those experienced in grantsmanship. They advised me my application would not succeed. I had explained that we didn’t know what experiments might deliver, and had acknowledged the technical problems that beset research and the possibility that competitors might solve problems before we did. My advisors said these admissions made the project look precarious and would sink the application. I was counselled to produce a detailed, but straightforward, program that seemed realistic—no matter if it were science fiction. I had not mentioned any direct application of our work: we were told a plausible application should be found or created. I was also advised not to put our very best ideas into the application as it would be seen by competitors—it would be safer to keep those ideas secret.

The peculiar demands of our granting system have favoured an upper class of skilled scientists who know how to raise money for a big group [3]. They have mastered a glass bead game that rewards not only quality and honesty, but also salesmanship and networking. A large group is the secret because applications are currently judged in a way that makes it almost immaterial how many of that group fail, so long as two or three do well. Data from these successful underlings can be cleverly packaged to produce a flow of papers—essential to generate an overlapping portfolio of grants to avoid gaps in funding.

Thus, large groups can appear effective even when they are neither efficient nor innovative. Also, large groups breed a surplus of PhD students and postdocs that flood the market; many boost the careers of their supervisors while their own plans to continue in research are doomed from the outset. The system also helps larger groups outcompete smaller groups, like those headed by younger scientists such as K. It is no wonder that the average age of grant recipients continues to rise [4]. Even worse, sustained success is most likely when risky and original topics are avoided and projects tailored to fit prevailing fashions—a fact that sticks a knife into the back of true research [5]. As Sydney Brenner has said, “Innovation comes only from an assault on the unknown” [6].

How did all this come about? Perhaps because the selection process is influenced by two sets of people who see things differently. The first are the granting organisations whose employees are charged to spend the money wisely and who believe that the more detailed and complex the applications are, the more accurately they will be judged and compared. Over the years, the application forms have become encrusted with extra requirements.

Universities have whole departments devoted to filling in the financial sections of these forms. Liaison between the scientists and these departments and between the scientists and employees of the granting agencies has become more and more Kafkaesque.

The second set of people are the reviewers and the committee, usually busy scientists who themselves spend much time writing grants. They try to do their best as fast as they can. Generally, each reviewer reads just one or two applications and is asked to give each a semiquantitative rating (“outstanding,” “nationally competitive,” etc.). Any such rating must be whimsical because each reviewer sees few grants. It is particularly difficult to rank strongly original grants; for no one will know their chances of success. The committee are usually presented with only the applications that have received uniformly positive reviews—perhaps favouring conventional applications that upset no one. The committee might have 30 grants to place in order of priority, which is vital, as only the top few can be funded. I wonder if the semiquantitative and rather spurious ratings help make this ordering just [7]. I also suspect any gain in accuracy of assessment due to the detail provided in the applications does not justify the time it takes scientists to produce that detail.


At the moment, young people need a paper as a ticket for the next step, and we should therefore give deserving, but unlucky, students another chance. One way would be to put more emphasis on open interviews (with presentation by the candidate and questions from the audience) and references. Not objective? No, but only false objectivity is offered by evaluating real people using unreal calculations with numbers of papers, citations, and journal impact factors. These calculations have not only demoralised and demotivated the scientific community [13], they have also redirected our research and vitiated its purpose [14].


Reading the piece, one can’t help but get the feeling that the current paradigm – “dark art” as the author puts it – is a lot like lobbying in politics! It isn’t enough for someone to have an interest in pursuing a research career. Being successful at it requires an in-depth understanding of a lot of the red-tape involved. Something that is such a fundamental aspect of academic life and yet that isn’t usually brought up – during career guidance talks, assessments of research aptitude, recruitment or what have you.

Do give the entire article a read. It’s worth it!

That does it for today. Until we meet again, cheers!

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Written by Firas MR

July 23, 2010 at 7:17 pm

On Literature Search Tools And Translational Medicine

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Courtesy danmachold@flickr (by-nc-sa license)

Howdy all!

Apologies for the lack of recent blogular activity. As usual, I’ve been swamped with academia.

A couple of interesting pieces on literature search strategies & tools that caught my eye recently, some of which were quite new to me. Do check them out:

  • Matos, S., Arrais, J., Maia-Rodrigues, J., & Oliveira, J. (2010). Concept-based query expansion for retrieving gene related publications from MEDLINE. BMC Bioinformatics, 11(1), 212. doi:10.1186/1471-2105-11-212


The most popular biomedical information retrieval system, PubMed, gives researchers access to over 17 million citations from a broad collection of scientific journals, indexed by the MEDLINE literature database. PubMed facilitates access to the biomedical literature by combining the Medical Subject Headings (MeSH) based indexing from MEDLINE, with Boolean and vector space models for document retrieval, offering a single interface from which these journals can be searched [5]. However, and despite these strong points, there are some limitations in using PubMed or other similar tools. A first limitation comes from the fact that keyword-based searches usually lead to underspecified queries, which is a main problem in any information retrieval (IR) system [6]. This usually means that users will have to perform various iterations and modifications to their queries in order to satisfy their information needs. This process is well described in [7] in the context of information-seeking behaviour patterns in biomedical information retrieval. Another drawback is that PubMed does not sort the retrieved documents in terms of how relevant they are for the user query. Instead, the documents satisfying the query are retrieved and presented in reverse date order. This approach is suitable for such cases in which the user is familiar with a particular field and wants to find the most recent publications. However, if the user is looking for articles associated with several query terms and possibly describing relations between those terms, the most relevant documents may appear too far down the result list to be easily retrieved by the user.

To address the issues mentioned above, several tools have been developed in the past years that combine information extraction, text mining and natural language processing techniques to help retrieve relevant articles from the biomedical literature [8]. Most of these tools are based on the MEDLINE literature database and take advantage of the domain knowledge available in databases and resources like the Entrez Gene, UniProt, GO or UMLS to process the titles and abstracts of texts and present the extracted information in different forms: relevant sentences describing a biological process or linking two or more biological entities, networks of interrelations, or in terms of co-occurrence statistics between domain terms. One such example is the GoPubMed tool [9], which retrieves MEDLINE abstracts and categorizes them according to the Gene Ontology (GO) and MeSH terms. Another tool, iHOP [10], uses genes and proteins as links between sentences, allowing the navigation through sentences and abstracts. The AliBaba system [11] uses pattern matching and co-occurrence statistics to find associations between biological entities such as genes, proteins or diseases identified in MEDLINE abstracts, and presents the search results in the form of a graph. EBIMed [12] finds protein/gene names, GO annotations, drugs and species in PubMed abstracts showing the results in a table with links to the sentences and abstracts that support the corresponding associations. FACTA [13] retrieves abstracts from PubMed and identifies biomedical concepts (e.g. genes/proteins, diseases, enzymes and chemical compounds) co-occurring with the terms in the user’s query. The concepts are presented to the user in a tabular format and are ranked based on the co-occurrence statistics or on pointwise mutual information. More recently, there has been some focus on applying more detailed linguistic processing in order to improve information retrieval and extraction. Chilibot [14] retrieves sentences from MEDLINE abstracts relating to a pair (or a list) of proteins, genes, or keywords, and applies shallow parsing to classify these sentences as interactive, non-interactive or simple abstract co-occurrence. The identified relationships between entities or keywords are then displayed as a graph. Another tool, MEDIE [15], uses a deep-parser and a term recognizer to index abstracts based on pre-computed semantic annotations, allowing for real-time retrieval of sentences containing biological concepts that are related to the user query terms.

Despite the availability of several specific tools, such as the ones presented above, we feel that the demand for finding references relevant for a large set of is still not fully addressed. This constitutes an important query type, as it is a typical outcome of many experimental techniques. An example is a gene expression study, in which, after measuring the relative mRNA expression levels of thousands of genes, one usually obtains a subset of differentially expressed genes that are then considered for further analysis [16,17]. The ability to rapidly identify the literature describing relations between these differentially expressed genes is crucial for the success of data analysis. In such cases, the problem of obtaining the documents which are more relevant for the user becomes even more critical because of the large number of genes being studied, the high degree of synonymy and term variability, and the ambiguity in gene names.

While it is possible to perform a composite query in PubMed, or use a list of genes as input to some of the IR tools described above, these systems do not offer a retrieval and ranking strategy which ensures that the obtained results are sorted according to the relevance for the entire input list. A tool more oriented to analysing a set of genes is microGENIE [18], which accepts a set of genes as input and combines information from the UniGene and SwissProt databases to create an expanded query string that is submitted to PubMed. A more recently proposed tool, GeneE [19], follows a similar approach. In this tool, gene names in the user input are expanded to include known synonyms, which are obtained from four reference databases and filtered to eliminate ambiguous terms. The expanded query can then be submitted to different search engines, including PubMed. In this paper, we propose QuExT (Query Expansion Tool), a document indexing and retrieval application that obtains, from the MEDLINE database, a ranked list of publications that are most significant to a particular set of genes. Document retrieval and ranking are based on a concept-based methodology that broadens the resulting set of documents to include documents focusing on these gene-related concepts. Each gene in the input list is expanded to its various synonyms and to a network of biologically associated terms, namely proteins, metabolic pathways and diseases. Furthermore, the retrieved documents are ranked according to user-defined weights for each of these concept classes. By simply changing these weights, users can alter the order of the documents, allowing them to obtain for example, documents that are more focused on the metabolic pathways in which the initial genes are involved.


(Creative Commons Attribution License:

  • Kim, J., & Rebholz-Schuhmann, D. (2008). Categorization of services for seeking information in biomedical literature: a typology for improvement of practice. Brief Bioinform, 9(6), 452-465. doi:10.1093/bib/bbn032
  • Weeber, M., Kors, J. A., & Mons, B. (2005). Online tools to support literature-based discovery in the life sciences. Brief Bioinform, 6(3), 277-286. doi:10.1093/bib/6.3.277

I’m sure there are many other nice ones out there. Don’t forget to also check out the NCBI Handbook. Another great resource …


On a separate note, a couple of NIH affiliated authors have written some thought provoking stuff about Translational Medicine:-

  • Nussenblatt, R., Marincola, F., & Schechter, A. (2010). Translational Medicine – doing it backwards. Journal of Translational Medicine, 8(1), 12. doi:10.1186/1479-5876-8-12


The present paradigm of hypothesis-driven research poorly suits the needs of biomedical research unless efforts are spent in identifying clinically relevant hypotheses. The dominant funding system favors hypotheses born from model systems and not humans, bypassing the Baconian principle of relevant observations and experimentation before hypotheses. Here, we argue that that this attitude has born two unfortunate results: lack of sufficient rigor in selecting hypotheses relevant to human disease and limitations of most clinical studies to certain outcome parameters rather than expanding knowledge of human pathophysiology; an illogical approach to translational medicine.


A recent candidate for a post-doctoral fellowship position came to the laboratory for an interview and spoke of the wish to leave in vitro work and enter into meaningful in vivo work. He spoke of an in vitro observation with mouse cells and said that it could be readily applied to treating human disease. Indeed his present mentor had told him that was the rationale for doing the studies. When asked if he knew whether the mechanisms he outlined in the mouse existed in humans, he said that he was unaware of such information and upon reflection wasn’t sure in any event how his approach could be used with patients. This is a scenario that is repeated again and again in the halls of great institutions dedicated to medical research. Any self respecting investigator (and those they mentor) knows that one of the most important new key words today is “translational”. However, in reality this clarion call for medical research, often termed “Bench to Bedside” is far more often ignored than followed. Indeed the paucity of real translational work can make one argue that we are not meeting our collective responsibility as stewards of advancing the health of the public. We see this failure in all areas of biomedical research, but as a community we do not wish to acknowledge it, perhaps in part because the system, as it is, supports superb science. Looking this from another perspective, Young et al [2] suggest that the peer-review of journal articles is one subtle way this concept is perpetuated. Their article suggests that the incentive structure built around impact and citations favors reiteration of popular work, i.e., more and more detailed mouse experiments, and that it can be difficult and dangerous for a career to move into a new arena, especially when human study is expensive of time and money.


(Creative Commons Attribution License:

Well, I guess that does it for now. Hope those articles pique your interest as much as they did mine. Until we meet again, adios :-) !

Copyright © Firas MR. All rights reserved.

Written by Firas MR

June 29, 2010 at 4:33 pm

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 :P !

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.

Keywords For Your Surgical Rotation In Med School

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An Ongoing Surgery

An Ongoing Surgery

Bonjour everyone! Today, I’m going to share with you some high yield keywords that should hopefully help you breeze through your surgical rotations in med school. Call it a checklist if you will. The objective is to facilitate memory recall and help you gear up with areas that you just have to familiarize yourself with, ideally before the start of your rotations. Understand that these are just keywords, with a special emphasis on surgical instruments, and you’ll really need to read some good books to develop your knowledge base. For a rapid-fire review I suggest Surgical Recall. For basic surgical skills, you might like RM Kirk’s Basic Surgical Techniques. It is also a good idea to refer to specific sections (for pictures of incisions, instruments, etc.) of a good reference book on the surgical specialty you’ll be rotating in. Finally, like we all know, surgery is an area that is incredibly skill based and different people have different preferences when carrying out the same thing – be it tying a knot, controlling a bleeder or what have you. You’ll learn to modify the way you do things depending on the specific ways of your surgical team.

I’ve also interspersed keywords specific to two areas that I have an interest in with regards to surgery, or rather surgical oncology to be exact – general thoracic surgery and colorectal surgery.

I shall be updating this list as the need comes. Comments, corrections and feedback are always welcome! Bye for now :-)!

Copyright © Firas MR. All rights reserved.

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Written by Firas MR

August 21, 2009 at 1:37 am

On The Impact Of Thinking Visually

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Romanesco broccoli - One of many examples of fractals in nature. (Wikipedia)

Romanesco broccoli - One of many examples of fractals in nature. (Wikipedia)

What do Mandelbrot and Einstein have in common?

They were/are both math aficionados. But more importantly, they both laid down the foundations for thinking about abstract natural phenomena in a geometrical way. The impact was reverberating.

Before Einstein came along, people had no real sense of gravity at all. Yea sure, there was Newton’s universal law of gravitation. But no one really could make any sense whatsoever of how exactly gravity might operate. Was it a wave? If so, at what speed could it act? Was there something particulate about it? Gravity was so mystical. And as always, so have been the concepts of time and space. Einstein’s greatest achievement in my view is that not only was he able to lay out the underpinnings of such phenomena in the form of a couple of abstract equations, but perhaps more importantly, that he devised a method to think about them visually. In developing his theories of special and general relativity, Einstein proposed the idea of the space-time fabric. It has a 3-D structure, yet represents four dimensions – 3 in space and 1 in time. Gravity would result from distortions in this fabric. The speed with which gravity could influence an object would depend on how fast these distortions could travel. And this central notion of ‘distortions in a fabric’ would also influence our understanding of the more difficult to grasp concepts of time and space. Time and space could mean different things to different observers depending on how this fabric was warped or sliced.

Mandelbrot achieved the same thing with his theory of fractals. How can complex natural structures and phenomena be represented mathematically? How to mathematically model a plant, the form of a human or a mountain range? In spite of how incredibly difficult it all sounds, these complex shapes could all be simplified into repeating units of tiny yet geometrically simple components – fractals. Mandelbrot went on to write his epic, “The Fractal Geometry Of Nature” and there was no turning back. Suddenly so many of nature’s workings could now be analyzed mathematically. An immensely significant step for mankind indeed. What I find absolutely fascinating about fractals, is the discovery that many intangible natural phenomena also contain a fractal component. Dr. Ary Goldberger and his team of researchers at Harvard Medical School have been working on applying fractal theory to medicine and biology. For those of you who might not be familiar with Dr. Goldberger, the name might ring a bell if you’ve read his books on electrocardiography. For Dr. Goldberger, interest in electrocardiography runs in the family, his father having invented the augmented limb leads back in the day. Among some of the things I learned about his work on electrocardiography, is that his team has shown that there is a fractal nature to ECG waveforms! This isn’t something like representing the heart itself in fractal form. It’s the activities of the heart that we are talking about here. Something really quite abstract. By looking at these fractal patterns, one could potentially detect pathology at a much earlier stage. Fractal patterns and their aberrations could help detect diseases in ways that no one had ever imagined! If you want to dig what’s cool, check out what’s been going on in the world of fractals in medicine – from human vasculature, to the brain and beyond. A quick PubMed query would lead you to a lot of riveting literature on the topic. Don’t forget to also take a look at the excellent documentary on fractal theory from PBS NOVA, “Hunting The Hidden Dimensions“.

Copyright © Firas MR. All rights reserved.

Readability grades for this post:

Flesch reading ease score: 61.1
Automated readability index: 8.6
Flesch-Kincaid grade level: 8.2
Coleman-Liau index: 10.8
Gunning fog index: 11.6
SMOG index: 11

Written by Firas MR

August 18, 2009 at 11:48 pm

Infusions Redux, DNS And Cerebral Edema

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Source, Author and License

There’s a book on fluid and electrolyte management that I’ve been reading recently. Called, “Practical Guideline on Fluid Therapy” and authored, as probably evident by the English used in the title, by a very Indian Sanjay Pandya, the book contains many interesting nuggets for day to day practice. Although like most Indian books there is a distinct absence of the emphasis on applying one’s brain, it is nevertheless worth the time to peruse. Today I will be discussing two equations from the book and a question that came up in my mind about the usage of a specific fluid.

Calculating ECF volume deficit (in dehydration, etc.)

  1. If the patient’s previous body weight is known, all you gotta do to obtain ECF deficit is find out the difference between his present and past weight.
  2. Another technique uses changes in the Hematocrit to discern ECF volume deficit. This method is applicable only if there is no hemorrhage, hemolysis or other situations involving loss of blood cells, the idea being that any change in blood volume is caused by plasma volume change. So if there’s dehydration and loss of ECF volume, plasma volume shrinks and causes the hematocrit to rise.

ECF Volume Deficit in liters = 0.2 * lean body weight * [(Current hematocrit/Desired hematocrit) - 1]

Can someone figure out the proof for the above equation and post it here? Like most other stuff, I absolutely hate roting math formulas and prefer remembering their derivations. This equation is taking me some time to prove.

To help get started, here are a couple of possible pointers I’m currently exploring:

Total body water (TBW) when expressed as a percentage of Total body weight (TBwt), varies by gender and age. In young adult men for example

TBW = 60% TBwt

TBW in liters

TBwt in kg

Interestingly enough, TBW when expressed as a percentage of lean body weight (LBwt) is a constant and isn’t conditioned upon gender or age.

TBW = 70% LBwt

LBwt = (100/70) * TBW

= (100/70) * [(x/100) * TBwt]

= (x/70) * TBwt

x is the percentage of TBwt that is TBW

Plasma volume is related to blood volume as follows

Plasma volume = Blood volume * [(100 - Hematocrit)/Hematocrit]

Plasma volume is also 1/4 of ECF volume. ECF is 1/3 of TBW. So plasma volume is 1/12 of TBW.

Calculating Electrolyte Infusion Rates

Change in plasma electrolyte concentration in mEq/L when 1 liter of  infusate is given

= [Infusate electrolyte concentration in mEq/L - Actual electrolyte concentration in mEq/L] / (TBW + 1)

This one’s easy to derive. Taking Na+ as our electrolyte example,

Initial Na+ content = x * TBW

Initial Na+ concentration = (x * TBW)/TBW

Final Na+ content after infusing 1L infusate = (x * TBW) + {y * 1}

Final Na+ concentration = [(x * TBW) + {y}]/(TBW + 1)

Change in Na+ concentration due to infusion = [(x * TBW) + {y}/(TBW + 1)] – [(x * TBW)/TBW]

= (yx)/(TBW+1)

x = mEq/L of Na+ initially in the body

y = mEq/L of Na+ in the infusate

And voila! There you have it!

And now for that promised question:

Given the fact that DNS (Dextrose Normal Saline) only stays in the ECF, would it be right to assume that it’s contraindicated in cerebral edema?

The interesting thing is that on exploring the scientific literature, I found that recent research shows that it isn’t just the shifting of fluid into the brain parenchyma that should be avoided when infusing fluid; hyperglycemia is a real danger as well. How hyperglycemia contributes to cerebral edema and especially in situations of cerebral ischemia is a topic of ongoing research and multiple plausible hypotheses are being investigated.

As per Pandya’s book, by the way, it is best to restrict glucose infusion to ≤ 0.5 grams/kg/hour when infusing any glucose containing fluid to avoid complications of hyperglycemia.

Readability grades for this post:

Kincaid: 11.4
ARI: 12.4
Coleman-Liau: 11.2
Flesch Index: 57.0/100
Fog Index: 14.6
Lix: 46.9 = school year 8
SMOG-Grading: 12.4

Copyright © Firas MR. All rights reserved.

Infusion Confusion – How To Calculate Drug Infusion Rates

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Source, author and license

The erosion of math and analytical skills that occurs with medics is truly astounding. Not surprising some might argue, what with it being such a memory oriented field. One area that many medics struggle with is drug dosage calculations. In the ER, one often doesn’t have the luxury of time and instant thinking is absolutely critical. Numbers need to be played out in seconds and optimal drug regimens have to be formulated. I was helping a colleague understand calculations for dopamine infusion the other day and thought like sharing with you folks some of the things we talked about.

Dopamine is used especially in ER settings to increase perfusion/blood pressure by means of its vasopressor, inotropic and chronotropic effects. When re-establishing blood pressure in a patient,  attention not only needs to be paid to drugs that might be used but also fluid replacement for any amount of fluid loss from the body. Two questions need to be asked before starting a dopamine infusion:

  1. How much dopamine?
  2. How much fluid and how fast?

The usual dosage of dopamine is somewhere between 5-10 μg/kg/min. For the following example I’ll use 10 μg/kg/min.

1μg = 0.001mg.

For a patient weighing x kg, the dosage is therefore 0.01x mg/min. Now that you’ve established how much dopamine you need to infuse per minute, here comes the second part.

Suppose you intend to infuse y ml of fluid (as part of the dopamine infusion, i.e. aside from any other fluid infusions already in place). Say also that you’ve added z mg of dopamine to form the infusate. Dopamine is supplied in liquid form, so any amount of dopamine occupies a certain volume in ml, which in most situations is negligible.

y ml of infusate = volume of Normal Saline, etc. + volume of dopamine

If z mg of dopamine is contained in y ml of infusate,

0.01x mg dopamine is contained in [0.01x/z] * y ml of infusate.

Thus you’re interested in giving [0.01x/z] * y ml of infusate every minute and a simple formula is derived where:

rate of dopamine infusion in ml/min = [0.01x/z] * y

and therefore, z = [0.01x/(rate of infusion in ml/min)] * y

x = body weight in kg

z = amount of dopamine added in mg

y = total volume of infusate in ml

For any drug infusion:

rate of infusion in ml/min = [(total drug dose in mg/min)/(amount of drug added in infusate in mg)] * volume of infusate in ml

This infusate is typically given via an infusion set that specifies a unique drops per ml ratio. At our pediatrics ER for example, infusion sets come in two forms – microdrip infusion sets (1 ml = 60 drops) and macrodrip infusion sets (1 ml = 20 drops). Simply multiply the rate of infusion in ml/min with 60 or 20 to get the infusion rate in drops/min for micro and macro IV sets respectively.

As seen from the formula above, when deciding to add a given amount of drug to form the infusate, three things need to be fixed first:-

  1. Dose of drug in the mg/min format (should be appropriate to the clinical condition of the patient).
  2. Total volume of infusate in ml (again, this depends on the clinical condition and hemodynamic stability of the patient).
  3. Speed or rate of fluid replacement in ml/min (this is important as sudden fluid-volume changes in the body can be problematic in certain cases and you want to go for a rate that is optimal, neither too slow nor too fast.)

And with that I end this post. Hope readers find this useful. Comments and corrections are welcome!

Readability grades for this post:

Kincaid: 8.4
ARI: 7.9
Coleman-Liau: 10.2
Flesch Index: 65.7/100 (plain English)
Fog Index: 12.7
Lix: 39.4 = school year 6
SMOG-Grading: 11.6

Copyright © Firas MR. All rights reserved.

Written by Firas MR

June 13, 2008 at 1:41 pm

USMLE Scores – Debunking Common Myths

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Source, Author and License

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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