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Contrasts In Nerdity & What We Gain By Interdisciplinary Thinking

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scientific fields and purity

Where Do You Fit In This Paradigm? (via xkcd CC BY-NC license)

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.

What Do You Care What Other People Think?: Further Adventures of a Curious Character by Richard Feynman as told to Ralph Leighton

an interdisciplinary web of a universe

An Interdisciplinary Web of a Universe (via Clint Hamada @ Flickr; CC BY-NC-SA license)

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.”



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Written by Firas MR

November 12, 2010 at 12:00 am

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!

Copyright © Firas MR. All rights reserved.

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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: 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 [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: 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.

Written by Firas MR

June 29, 2010 at 4:33 pm

The Doctor’s Apparent Ineptitude

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ineptitude

via Steve Kay@Flickr (by-nc-nd license)

As a fun project, I’ve decided to frame this post as an abstract.

AIMS/OBJECTIVES:

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.

RESULTS:

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.

Patient A

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!’

Patient B

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

Although numerous psychology models can be applied to understand physician behavior, the Freudian model reveals some interesting material. Common defense strategies that help doctors include:

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.

All of these form a systematic ego-syntonic pattern of behavior, but for reasons we are still exploring, is not included in the DSM-IV manual’s section on Personality Disorders.

CONCLUSIONS:

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.

USMLE – Designing The Ultimate Questions

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Question

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

 

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

The Multi-Step

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

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

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

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

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

The Bait-and-Switch

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

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

Copyright © Firas MR. All rights reserved.

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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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