From Dr. Jill Bolte Taylor: The Most Electrifying Lecture You'll Ever Hear

I've heard a lot of lectures in my life but never one like this. This is neuroanatomist Dr. Jill Bolte Taylor giving a talk on the functional differences between the right and left hemispheres of the brain. She's passionate about her subject as she actually suffered a massive intracranial hemorrhage that trashed her left brain. Dr. Taylor brilliantly describes the experience and sensations both as a stroke victim and as a scientist.

Not that I want to replicate her experience but man do I wish I could teach like her! Check it out and prepare to be riveted by the most amazing 20 minute talk you've ever heard.

It makes me never want to step up to a podium again.

Expanding Your Vocabulary With HotForWords

I love words and have always enjoyed expanding my vocabulary whenever possible. I confess a certain pleasure in being facile with medical terminology especially when it puts most laypersons completely in the dark. It's that whole belonging to a secret club thing I guess. But despite that guilty pleasure, knowing mainstream English words that no one else knows holds far more interest for me.

When I was faced with the daunting task of increasing my vocabulary for the Graduate Record Examination (required for applying to a master's program in public health), I took a rather unusual approach. Rather than study one of the many GRE prep books, I read Francis Bacon's Advancement of Learning. This rather deep book on epistemology (the science of knowledge for those with smaller vocabularies than mine) was so dense, that I had to look up at least one word in the dictionary on virtually every page. By the time I was finished, there was NO way that I wasn't going to excel on at least the verbal part of that test.

Well, I may have found a better way and her name is Marina also known as HotForWords.

I frankly don't recall how I came across her on the internet but I did. Marina is a philologist, one who studies etymology and linguistics. She has taken it upon herself to convey her passion for words to us neophytes and her approach to education is...refreshing? Marina's style is perhaps most effective at stimulating male enthusiasts of the English language but certainly women will have much to learn from her as well. For kids...I think I'd go with more traditional methods.

She has posted some 58 videos on youtube, each one a short seminar on the etymology of a different word. I can't help but think that if I'd had her help so many years ago, I would have done even better on my test. The complete collection can be found here. I warn you though; they push the envelope on being "workplace safe".

I invite you to partake of this video where Professor Marina explains the origin of one of my favorite words, pusillanimous.


Also try out this very informative one on irony.

Pay attention.

Drinking, Driving, and Little Kids

It's hard to be a kid in school today and if I'm right about this, it's going to become even harder. A study published in the latest edition of Accident; Analysis and Prevention reveals that the problem of impaired driving may be bigger in middle school children than was previously thought.

17% of 290 middle school children surveyed admitted to driving after drinking. Maybe I'm just out of the loop but I thought that ten to thirteen-year-olds weren't supposed to drink OR drive.

If this study is accurate and generalizable, (it was limited to one Mississippi school in a rural area) there will be repercussions.

I can easily imagine a referendum to mandate teaching about the burdens of DUI's to young school kids. Such instruction will almost surely be useless and ineffective. It will also undoubtedly sidestep the issue of parental responsibility.

Given the fatalistic approach we've taken towards sex education (don't have sex but if you do, here's how...) I'm pretty sure I know what form this will take.

Class time is going to be set aside for lectures on the perils of mixing gin with your kool-aid and how doing so will degrade one's ability to negotiate freeway onramps. I can just picture the "role-playing" scenarios where the independent-minded little tyke has to learn to demand the car keys from her inebriated tetherball partner.

I'm just worried about when there'll be any time at all to study reading, writing, and arithmetic. Maybe in the interest of efficiency, they can have a session of interpreting blood alcohol levels incorporated into their math classes?

Choking on Caution

You may be interested in my latest article in TCSDaily.com.

When I was a kid, forced silence is school was considered a punishment for when we were bad. The times they are a changing.

Liars, Clinical Tests and a Bit of Math

This next post is of virtually no general interest whatsoever. I'm being totally self-indulgent in that I really like this stuff but hey, it's my blog! Some clinicians may be a little interested however. We shall see.

Bryan Caplan of EconLog posted on our ability to distinguish truth tellers from liars. It's not as good as we might think. According to one study cited "People correctly identify truths 70% of the time and correctly identify lies only 50% of the time."

If lies are the disease, then this corresponds to what statisticians refer to as a sensitivity of 50% and a specificity of 70%. Please note that I'm restating the more pessimistic analysis of Kaplan's where truth is considered the disease.

Both of these performance indicators are pretty bad by medical standards. You'd definitely hope that that expensive spiral CT was better at detecting a serious blood clot in your lung!

The concept of test sensitivity and specificity is confusing to a lot of people especially doctors. Although it's a simplification, for most purposes, these numbers reflect only the test under consideration. What clinicians (and in today's example, CIA interrogators) are mainly interested is predictive ability, the ability of a test to predict the probability that a person has or doesn't have a particular disease (or is telling a fib).

We use knowledge of a test's sensitivity and specificity to help us with these predictions using a horrible piece of mathematics called Bayes' theorem.

To use the example here, assume a person lies 50% of the time. This is called the pre-test probability (or prevalence) of lying. This is about what we'd expect in a politician. With the above sensitivity and specificity, Bayes' theorem would predict the following:

If a person "seems" to be lying, there's a 63% post-test probability that he is.
If a person "seems" to be truthful, theres a 58% post-test probability that he is.

So given a 50% probability of lying in the first place, after actually observing the person, if you suspect a lie, our post-test conviction has risen only from 50% to 63%. Not very good in my opinion.

If we suspect truthfulness, our post-test conviction of truthfulness only rises from 50% to 58%. It would appear that we're worse at detecting truth.

One problem with this kind of analysis is that it is not very intuitive. juggling around sensitivities and specificities requires some mental gymnastics that few of us are capable of. Many different combinations of sensitivity and specificity (and probabilities of having the disease in the first place) will yield similar or different predictive abilities of the test. What is needed is a framework for thinking about tests that allows us to combine sensitivity and specificity into one number which can then be thought of independently from the disease prevalence.

Fortunately such a framework was published (at least in the medical literature) in 1999 here and here. These articles are not for the faint of heart! On the other hand, the concepts the author discusses actually make this whole thing easier. He's incorporated sensitivity and specificity, two somewhat nebulous concepts into two indicators: a positive and a negative likelihood ratio (PLR and NLR) which even an internist can understand.

Likelihood ratios are calculated from the sensitivity and specificity using some simple formulas. You can find the formulas here. You can also find an online calculator that does this for you here. I only want to discuss this conceptually and demonstrate how LR's are used.

When using LR's, you think in terms of odds instead of probability. This is the way things are done in Las Vegas. Instead of saying that there's a 50% chance a coin will come up heads, we say the odds are 1 to 1 (1/1 or 1.0). Instead of saying that the odds of a (non-crooked) die coming up 5 is 1/6 or 17% we say the odds are 1 to 5 (1/5 or .20).

The reason for using odds instead of probability is that the relationships between pre-test odds and post-test odds becomes VERY straightforward mathematically. No horrible Bayes' Theorem! The relationships are here:

If the test is positive:
(pre-test odds of condition) X(PLR) = (post-test odds of condition)

If the test is negative:
(pre-test odds of condition)X(NLR) = (post-test odds of condition-free)

This makes things easy to understand. In our example above, the PLR is 1.7. Use the online calculator to calculate this (and use proportions instead of percentages). This means that the odds of someone telling a lie increases by a factor of 1.7 if you think he looks dishonest.

No complicated Bayesian analysis. No fiddling with sensitivity or specificity. In fact, you don't even have to know the pre-test odds of disease. You just know that on the basis of the above-cited data, the post-test odds of lying is 1.7 times greater if you suspect lying. This gives you a better mental idea of how good the test is.

Likewise, the NLR is 0.7. So if you suspect that the person is telling the truth (a negative test), then the odds that he's lying go down to 0.7 times the pre-test odds.

Have I hopelessly confused you all? I hope not. The reason I mention all this is because once you get the hang of it, thinking in terms of LR's is much easier than thinking in terms of sensitivity and specificity. The medical literature increasingly calculates and cites the LR's for tests. Hopefully now you'll understand why they are useful.

If you're interested in this topic, I'd strongly suggest reading the link I cited above.

Probability and Medicine

EconLog, linked an article by Dr. Richard Friedman on probability and medicine. In it, Friedman makes the point that many patients don't really understand the nature of probability in medical decision making. He cites the confusion a patient had when she was trying to understand that a 60% response rate with a given anti-depressant didn't mean that she would respond to it 60% of the time.

When he explained that she would either respond or not respond, she became confused and said "You mean my chances of getting better are really only 50%?" Clearly, she was mistaking the binary aspect of the treatment outcome (getting better or not) with the probability that she herself would get a response.

Friedman then speculated on why her patient might have had this misconception. He points out that mathematicians have attributed such problems on innumeracy "the arithmetic equivalent of illiteracy". He also mentioned that some misunderstanding might arise from a natural human tendency to not attribute bad (or any striking) events to chance.

Personally, I think the example Friedman cited has more to do with innumeracy. However, I don't like the word because of its pejorative connotation. The fact is that most of us have this type of innumeracy even doctors (if you can believe it). Probability is one of those terribly difficult philosophical problems that trouble just about all of us.

The dynamics of a clinical situation will determine the probability of a given patient developing a specific disease. A smoker has a higher probability of getting lung cancer than a nonsmoker, but an individual will either get it or not period. This sounds straight forward but a lot of people have problems with it. Some smokers never get lung cancer and some nonsmokers do. The reason is that smoking is not the only factor that leads to lung cancer. The more factors we understand (for example age, exposure to other toxins such as oxidants and genetics), the more precise the probability estimate will become.

This becomes very important as physicians increasingly embrace evidence-based medicine (EBM). In the desire to cite statistics of medical outcomes (such as the chance of developing a certain disease or the likelihood that a certain treatment will work) it is very important to recognize that every patient is different. The study population of a particular study will surely have a cross-section of many different types of participants. The patient's observed probability will be closer to patients more like himself -- maybe closer in ways that weren't even imagined or assessed by the researchers.

The original studies looking at the impact of cholesterol on cardiac outcomes didn't subdivide patients by measuring the different types of cholesterol such as LDL, HDL or triglycerides. Had they done so, individual probabilities of adverse outcomes could have been better stratified (as they have been subsequently).

As physicians, we have to do a better job of explaining these concepts to our patients. At the same time, we need to do a better job of understanding them ourselves! What's true in a study may not be true for a particular patient.

I want to close this post with my favorite probability brainteaser: If you flip a coin nine times and it comes up heads each time, what is the probability that it will come up heads the tenth time? I'll put the correct answer as the first comment to this post.