Archive for the ‘Medical Education’ Category
I’ve always been struck by how nerds can act differently in different fields.
An art nerd is very different from a tech nerd. Whereas the former could go on and on about brush strokes, lighting patterns, mixtures of paint, which drawing belongs to which artist, etc. the latter can engage in ad-infinitum discussions about the architecture of the internet, how operating systems work, whose grip on Assembly is better, why their code works better, etc.
And what about math and physics nerds? They tend to show their feathers off by displaying their understanding of chaos theory, why imaginary numbers matter, and how we are all governed by “laws of nature”, etc.
How about physicians and med students? Well, like most biologists, they’ll compete with each other by showing off how much of anatomy, physiology or biochemistry or drug properties they can remember, who’s uptodate on the most recent clinical trial statistics (sort of like a fan of cricket/baseball statistics), and why their technique of proctoscopy is better than somebody else’s, the latest morbidity/mortality rates following a given procedure, etc.
And you could actually go on about nerds in other fields too – historians (who remembers what date or event), political analysts (who understands the Thai royal family better), farmers (who knows the latest in pesticides), etc.
Each type has its own traits, that reflect the predominant mindset (at the highest of intellectual levels) when it comes to approaching their respective subject matter. And nerds, being who they are, can tend to take it all to their heads and think they’ve found that place — of ultimate truth, peace and solace. That they are at last, “masters” of their subjects.
I’ve always found this phenomenon to be rather intriguing. Because in reality, things are rarely that simple – at least when it comes to “mastery”.
In medicine for instance, the nerdiest of most nerds out there will be proud and rather content with the vast statistics, nomenclature, and learn-by-rote information that he has finally been able to contain within his head. Agreed, being able to keep such information at the tip of one’s tongue is an achievement considering the bounds of average human memory. But what about the fact that he has no clue as to what fundamentally drives those statistics, why one drug works for a condition whereas another drug with the same properties (i.e. properties that medical science knows of) fails or has lower success rates, etc.? A physicist nerd would approach this matter as something that lies at the crux of an issue — so much so that he would get sleepless nights without being able to find some model or theory that explains it mathematically, in a way that seems logical. But a medical nerd? He’s very different. His geekiness just refuses to go there, because of the discomforting feeling that he has no idea whatsoever! More stats and names to rote please, thank you!
I think one of the biggest lessons we learn from the really great stalwarts in human history is that, they refused to let such stuff get to their heads. The constant struggle to find and maintain humility in knowledge was central to how they saw themselves.
… I can live with doubt and uncertainty and not knowing. I think it’s much more interesting to live not knowing than to have answers which might be wrong. I have approximate answers and possible beliefs and different degrees of certainty about different things, but I’m not absolutely sure of anything and there are many things I don’t know anything about, such as whether it means anything to ask why we’re here, and what the question might mean. I might think about it a little bit and if I can’t figure it out, then I go on to something else, but I don’t have to know and answer, I don’t feel frightened by not knowing things, by being lost in a mysterious universe without having any purpose, which is the way it really is so far as I can tell. It doesn’t frighten me.
— Richard Feynman speaking with Horizon, BBC (1981)
The scientist has a lot of experience with ignorance and doubt and uncertainty, and this experience is of great importance, I think. When a scientist doesn’t know the answer to a problem, he is ignorant. When he has a hunch as to what the result is, he is uncertain. And when he is pretty darn sure of what the result is going to be, he is in some doubt. We have found it of paramount importance that in order to progress we must recognize the ignorance and leave room for doubt. Scientific knowledge is a body of statements of varying degrees of certainty – some most unsure, some nearly sure, none absolutely certain.
Now, we scientists are used to this, and we take it for granted that it is perfectly consistent to be unsure – that it is possible to live and not know. But I don’t know everybody realizes that this is true. Our freedom to doubt was born of a struggle against authority in the early days of science. It was a very deep and very strong struggle. Permit us to question – to doubt, that’s all – not to be sure. And I think it is important that we do not forget the importance of this struggle and thus perhaps lose what we have gained.
Besides being an important aspect for high-school students to consider when deciding what career path to pursue, I think that these nerd-personality-traits also illustrate the role that interdisciplinary thinking can play in our lives and how it can add tremendous value in the way we think. The more one diversifies, the more his or her thinking expands — for the better, usually.
Just imagine a nerd who’s cool about art, physics, math or medicine, etc. — all put together, in varying degrees. What would his perspective of his subject matter and of himself be like? Would he make the ultimate translational research nerd? It’s not just the knowledge one could potentially piece together, but the mindset that one would begin to gradually develop. After all, we live in an enchanting web of a universe, where everything intersects everything!
Copyright Firas MR. All Rights Reserved.
“A mote of dust, suspended in a sunbeam.”
Search Blog For Tags: education, medicine, medical education, research, science, translational research
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.
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,
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:
- 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?
- 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.
- 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?
- Doesn’t the language we use influence how we think?
- 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!
- 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! :-)
- 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.
- 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.
- Understand the peer-review process in science and its benefits and short-comings.
- 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.
- 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.
- 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:
- 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! :-)
- 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.
- 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.
- 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.
- 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.
- 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!
- 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.
- 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.
- 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! :-)
- 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.
- 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.
- 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! :-)
- 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!
- 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.
- It’s important to develop a level of honesty and integrity that can withstand the onward thrust of cargo-cult science.
- 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.
- 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 …”
- 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.”
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:
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 . 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 . 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 . 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 . As Sydney Brenner has said, “Innovation comes only from an assault on the unknown” .
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 . 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 , they have also redirected our research and vitiated its purpose .
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!
Copyright © Firas MR. All rights reserved.
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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 . 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 . 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  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 . 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 , which retrieves MEDLINE abstracts and categorizes them according to the Gene Ontology (GO) and MeSH terms. Another tool, iHOP , uses genes and proteins as links between sentences, allowing the navigation through sentences and abstracts. The AliBaba system  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  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  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  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 , 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 , 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 , 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: http://creativecommons.org/licenses/by/2.0)
- 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  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: http://creativecommons.org/licenses/by/2.0)
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.
As a fun project, I’ve decided to frame this post as an abstract.
To elucidate factors influencing perceived incompetence on the part of the doctor by the layman/patient/patient’s caregiver.
MATERIALS & METHODS:
Arm-chair pontification and a little gedankenexperiment based on prior experience with patients as a medical trainee.
Preliminary analyses indicate widespread suspicions among patients on the ineptitude of doctors no matter what the level of training. This is amply demonstrated in the following figure:
As one can see, perceived ineptitude forms a wide spectrum – from most severe (med student) to least severe (attending). The underlying perceptions of incompetence do not seem to abate at any level however, and eyewitness testimonies include phrases such as ‘all doctors are inept; some more so than others’. At the med student level, exhausted patients find their anxious questions being greeted with a variety of responses ranging from the dumb ‘I don’t know’, to the dumber ‘well, I’m not the attending’, to the dumbest ‘uhh…mmmm..hmmm <eyes glazed over, pupils dilated>’. Escape routes will be meticulously planned in advance both by patients and more importantly by med students to avert catastrophe.
As for more senior medics such as attendings, evasion seems to be just a matter of hiding behind statistics. A gedankenexperiment was conducted to demonstrate this. The settings were two patients A and B, undergoing a certain surgical procedure and their respective caregivers, C-A and C-B.
Consent & Pre-op
C-A: (anxious), Hey doc, ya think he’s gonna make it?
Doc: It’s difficult to say and I don’t know that at the moment. There are studies indicating that 95% live and 5% die during the procedure though.
C-A: ohhh kay (slightly confused) (murmuring)…’All this stuff about knowing medicine. What does he know? One simple question and he gives me this? What the heck has this guy spent all these years studying for?!’
Post-op & Recovery
C-A: Ah, I just heard! He made it! Thank you doctor!
Doc: You’re welcome (smug, god-complex)! See, I told ya 95% live. There was no reason for you to worry!
C-A: (sarcastic murmur) ‘Yeah, right. Let him go through the pain of not knowing and he’ll see. Look at him, so full of himself – as if he did something special; luck was on our side anyway. Heights of incompetence!’
Consent & Pre-op
C-B: (anxious) Hey doc, ya think he’s gonna make it?
Doc: It’s difficult to say and I don’t know that at the moment. There are studies indicating that 95% live and 5% die during the procedure though.
C-B: ohhh kay (slightly confused) (murmuring)…’All this stuff about knowing medicine. What does he know? One simple question and he gives me this? What the heck has this guy spent all these years studying for?!’
Post-op & Recovery
C-B: (angry, shouting numerous explicatives) What?! He died on the table?!
Doc: Well, I did mention that there was a 5% death rate.
C-B: (angry, shouting numerous explicatives).. You (more explicatives) incompetent quack! (murmuring) “How convenient! A lawsuit should fix him for good!”
The Doctor’s Coping Strategy
Isolation of affect: eg. Resident tells Fellow, “you know that patient with the …well, she had a massive MI and went into VFib..died despite ACLS..poor soul…so hey, I hear they’re serving pizza today at the conference…(the conference about commercializing healthcare and increasing physician pay-grades for ‘a better and healthier tomorrow’)”
Intellectualization: eg. Attending tells Fellow, “so you understand why that particular patient bled to death? Yeah it was DIC in the setting of septic shock….plus he had a prior MI with an Ejection Fraction of 33% so there was that component as well..but we couldn’t really figure out why the antibiotics didn’t work as expected…ID gave clearance….(ad infinitum)…so let’s present this at our M&M conference this week..”
Displacement: eg. Caregiver yells at Fellow, “<explicatives>”. Fellow yells at intern, “You knew that this was a case that I had a special interest in and yet you didn’t bother to page me? Unacceptable!…” Intern then yells at med student, “Go <explicatives> disimpact Mr. X’s bowels…if I don’t see that done within the next 15 minutes, you’re in for a class! Go go go…clock’s ticking…tck tck tck!”
We believe there are other coping mechanisms that are important too, but in our observations these appear to be the most common. Of the uncommon ones, we think med students as a group in particular, are the most vulnerable to Regression & Dissociation, duly accounting for confounding factors.
Patients and their caregivers seem to think that ALL doctors are fundamentally inept, period. Ineptitude follows a wide spectrum however – ranging from the bizarre to the mundane. Further studies (including but not limited to arm-chair pontification) need to be carried out to corroborate these startling results and the factors that we have reported. Other studies need to elucidate remedial measures that can be employed to save the doctor-patient relationship.
NOTE: I wrote this piece as a reminder of how the doctor-patient relationship is experienced from the patient’s side. In our business-as-usual frenzy, we as medics often don’t think about these things. And these things often DO matter a LOT to our patients!
Copyright © Firas MR. All rights reserved.
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’.
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:
- 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 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 !
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.
Just a quick thought. It just occurred to me that some of the questions on the USMLE involving pedigree analysis in genetics, are actually typical decision tree questions. The probability that a certain individual, A, has a given disease (eg: Huntington’s disease) purely by random chance is simply the disease’s prevalence in the general population. But what if you considered the following questions:
- How much genetic code do A and B share if they are third cousins?
- If you suddenly knew that B has Huntington’s disease, what is the new probability for A?
- What is the disease probability for A‘s children, given how much genetic code they share with B?
When I’d initially written about decision trees, it did not at all occur to me at the time how this stuff was so familiar to me already!
Apply a little Bayesian strategy to these questions and your mind is suddenly filled with all kinds of probability questions ripe for decision tree analysis:
- If the genetic test I utilize to detect Huntington’s disease has a false-positive rate x and a false-negative rate y, now what is the probability for A?
- If the pre-test likelihood is m and the post-test likelihood is n, now what is the probability for A?
I find it truly amazing how so many geneticists and genetic counselors accomplish such complex calculations using decision trees without even realizing it! Don’t you :-) ?
Copyright © Firas MR. All rights reserved.