It gives me no pleasure to delete a feed that I have been following off and on for more than a decade now, but the logorrhea combined with intentional scandal-seeking behavior has become too much. Screen after awful screen of gotchas and callouts in a single post. Whatever for?
Quote of the morning:
My view is that any theory of what is wrong with American health care is true because American health care is wrong in every possible way.
There is both a science and an art to medicine. The “art” part usually comes into play when we talk about bedside manner and the doctor-patient relationship, but recognizing and naming diseases — diagnosis — is also up there. José Luis Ricón wrote recently about a fairly discrete entity, Alzheimer’s disease, and how several different paths may lead to a similar phenotype. This is true for most diseases.
But take something like “cytokine release syndrome”, or “HLH”, or any other syndromic disease that is more of a suitcase phrase than anything, and that can present as a spectrum of symptoms. Different paths to different phenotypes, with only a sleigh of molecular storytelling to tie them together. Yet somehow it (mostly) works. It’s quite an art.
I just finished attending a 1-hour career panel for UMBC undergrads thinking about medical school, and the one thing anyone interested in practicing medicine in America should know is that you really, really, really need to know how to answer multiple choice questions. It doesn’t matter how smart, knowledgable, or hard-working you are: if you don’t have the skills needed to pick the one correct answer out of the four to six usually given, be ready to take a hit on how, where, and whether at all you can practice medicine in the US.
To be clear, this is a condemnation of the current system! Yes, there are always tradeoffs: oral exams so prevalent in my own medical school in Serbia weight against the socially awkward and those who second-guessed themselves. But the MCQs are so pervasive in every aspect of evaluating doctors-to-be (and practicing physicians!) that you have to wonder about all the ways seen and unseen in which Goodhart’s law is affecting healthcare.
What would the ideal evaluation of medical students look like? It wouldn’t rely on a single method, for one. Or, to be more precise, it wouldn’t make a single method the only one that mattered. Whether it’s the MCAT to get into medical school, USMLE to get into residency and fellowship, or board exams to get and maintain certification, it is always the same method for the majority of (sub)specialties. Different organizations, at different levels of medical education, zeroing in on the same method could indeed mean that the method is really good — see: carcinisation — To save you a click: it is “a form of convergent evolution in which non-crab crustaceans evolve a crab-like body plan”, as per Wikipedia. In other words, the crab-like body plan is so good that it evolved at least five different times. but then if it is so great to be shaped like a crab, where are our crab-like overlords?
Being a crab is a great solution for a beach-dwelling predatory crustacean with no great ambitions, and MCQs are a great solution to quickly triage the abysmal from everyone else when you are pressed for resources and time. But, both could also be signs of giving up on life, like how moving to your parents' basement is the convergence point for many different kinds of failed ambition.
Behind the overuse of MCQs is the urge to rank. Which, mind you, is not why tests like USMLE were created. They were, much like the IQ tests, meant to triage the low-performing students from the others. But the tests spits out a number, and since a higher number is by definition, well, higher than the lower ones, the ranking began, and with it the Goodhartization of medical education. The ranking became especially useful as every step of the process became more competitive and the programs started getting drowned in thousands of applications, all with different kinds of transcripts, personal statements, and letters of recommendation. The golden thread tying them all together, the one component to rule them all, was the number they all shared — the USMLE score.
But then the programs started competing for the same limited pool of good test-takers, neglecting the particulars of why a lower-scoring candidate may actually be a better match for their program. Bad experience all around, unlike you are good at taking tests, in which case good for you, but also look up bear favor. On the other hand, there is all this other information — words, not numbers — that gets misused or ignored. If only there was a way for medical schools and residency programs to analyze the applications of students/residents that they found successful by whatever metric and make a tailor-made prediction engine.
Which is kind of like what machine learning is, and it was such a logical thing to do that of course people tried it, several times, with mixed success. It was encouraging to see that two of these three papers were published in Academic Medicine, which is AAMCs own journal. One can only hope that this will lead to a multitude of different methods of analysis, a thousand flowers blooming, etc. The alternative — one algorithm to rule them all — could be as bad as USMLE.
The caveat is that Americans are litigious. Algorithmic hiring has already raised some alarm, so I can readily imagine the first lawsuit from an unmatched but well-moneyed candidate complaining about no human laying their eyes on the application. But if that’s the worst thing that could happen, it’s well-worth trying.
We need the FDA to be more insulated from these forces. Instead, every few years, legislators offer bills that amount to death by a thousand cuts for the agency. The latest is the Promising Pathways Act, which offers “conditional approval” of new drugs, without even the need for the preliminary evidence that accelerated approval requires (i.e., some indication that biomarkers associated with real outcomes like disease progression or survival are moving in the right direction in early drug studies).
This bill is being pushed by powerful patient groups and has the support of Democratic senators like Kristin Gillibrand and Raphael Warnock, who should know better.
I can see how it may appeal to patients: you may get a promising new drug for your life-threatening, debilitating disease sooner via this pathway. On the other hand, there are already mechanisms in place that enable access to these: a clinical trial, for one. Or expanded access (a.k.a. “compassionate use”) for those who may not be eligible for a trial.
So how would “provisional approval” help? If anything, wouldn’t it transfer the risks and — importantly — costs of drug development from the drug manufacturer/sponsor/study investigator to the patient?
Ultimately, the reason why there aren’t many cures for rare, terminal diseases is not because the big bad FDA is keeping the already developed drugs away from patients but rather because they are devilishly difficult to develop at our current level of technology. Wouldn’t it then make more sense to work on advancing the technology The careful reader will note that the opposite is being done, and I write this as no great fan of AI. that would lead to those new cures? I worry that the Promising Pathways Act would solve a problem that doesn’t exist by adding to the already skyrocketing costs of American health care. But that could be just me.
Do you know about a horse named Jim? The one whose tetanus-contaminated serum was used to make diphtheria antitoxin that killed kids:
These failures in oversight led to the distribution of antitoxin that caused the death of 12 more children, which were highly publicized by newspaper magnate Joseph Pulitzer as part of his general opposition to the practice of vaccination.
There is a straight line from Jim to the creation of US FDA, in case you want to remove that particular Chesterton’s fence.
Seth Godin on the amateur presenter:
If you’re called on to give a talk or presentation, the biggest trap to avoid is the most common: Decide that you need to be just like a professional presenter, but not quite as good. Being a 7 out of 10 at professional presenting is a mistake. Better to stay home and send a memo.
This is exactly what happened last month at that medical conference. Colleagues, please stop.
Our clinical trials course at UMBC is well under way, and we are getting some terrific questions from students. Here is one!
Q: Are outcomes surrogate endpoints or is there a distinction between the two?
The terms “outcome” and “endpoint” are not strictly defined and some people use them interchangeably. However:
- Outcomes are broader, and include any change in health that are considered important enough to measure in a patient (such as “overall survival” — the amount of time between enrolling onto the trial and death, or “quality of life” — a certain score on a specified scale that a patient fills out or the doctor performs).
- Endpoints are more specific than outcomes, consider the whole study population instead of individual patients, and need to have a precisely defined way of measurement and time points when they are measured (e.g. “median overall survival”, “3-year overall survival rate”, and “5-year overall survival rate” are three different endpoints that are different ways of aggregating and evaluating the same individual patient outcome — overall survival).
It reminds me of the confusion between efficacy and effectivness, only it’s worse: there is no agreed-upon text that describes the distinction, so it is a really terminological free-for-all. Indeed, what I wrote above may end up not being true — caveat lector! As always, it is always best to ask people to clarify what they meant when they said this or that. Regardless, if someone tells you that “overall survival” (or, worse yet, “survival”) was the primary endpoint, it clearly can’t be the case. Endpoints need to be more specific than that.
Surrogate outcomes and surrogate endpoints are those which are stand-ins for what we actually care about. Here is a good video on surrogate endpoints in oncology.E.g. when we give chemotherapy to someone with cancer, we do it so that they would live longer and/or better. However, it is quicker and easier to measure if the tumor shrinks after chemotherapy (i.e. “responds” to treatment), and we believe that the tumor shrinking will lead to the patient living longer or better (which may not necessarily be the case!), so we use the response as a surrogate outcome for survival and quality of life (by how much did the tumor shrink? was it a complete or a partial response according to pre-specified criteria?). Study level surrogate endpoints would be the overall response rate, partial response rate, complete response rate, etc.
We have created so much confusion here that it is a small miracle we can communicate amongst ourselves at all.
Michael Bonner on MAID, Canada’s euthanasia program:
In Canada, patients can wait years for medical treatment in the country’s overburdened and underfunded health-care system. The baby boomer population, as it ages, will only increase the strain on the medical system, the welfare state, and the Canada Pension Plan. A 2017 study published in the Canadian Medical Association Journal suggested euthanasia could save the country between $34.7 million and $138.8 million annually—a “substantial savings,” the authors said. Is this why the government is eager to expand MAID?
Kurt Vonnegut, 2 B R 0 2 B:
Got a problem? Just pick up the phone. It solved them all — and all the same way!
Next Wednesday looks busy, but there is a Thanksgiving-sized gap in the calendar.
- Development of a CMV Vaccine: From Bench to Congress by Sallie Permar, MD PhD; Wednesday November 8 at 12pm EST
- Unraveling Regulation and Unexpected Role of Interleukin 17 by Huyn Park; Wednesday November 8 at 4pm EST
- Nanomedicine: Which Cancerst to Treat by Gregory Lanza, MD PhD is a 3-hour workshop that is free to public requires pre-registration; held today at 2pm EST, and if you can’t make it there is one on Wednesday, November 8 and Tuesday, November 14, also at 2pm EST
- Exploratory and Inferential Spatial Statistical Methods: Tools to Understand the Geography of Health Across the U.S. by Loni Philip Tabb, PhD; Tuesday, November 18 at 1pm EST, open to public by requires registration