Heads and tails and a liver

A physician is obligated to consider more than a diseased organ, more even than the whole man — he must view the man in his world.

- Harvey Cushing

Be careful, when you get into practice, to cultivate equally well your hearts and your heads.

- William Osler

In 2020, I designed a small online survey called “Distributive Justice in Healthcare”, inspired by the research by Ubel et al [1]. The idea was born out of the early COVID-19 pandemic — a time when ventilators, ICU beds, and even basic protective equipment became scarce overnight. Suddenly, triage became the norm, and difficult decisions had to be made about who gets access to these resources. I found myself wondering how I would make such decisions if I were in charge, which got me into the medical ethics literature.

Distribution of scarce medical resources is most relevant in the context of organ transplants. The triage calculations that happened during COVID happen quietly in every organ transplant committee. Before anyone is even placed on the transplant list, they go through a detailed assessment not only of their medical condition but also of their behavioral and psychosocial factors. Such assessments raise two major problems: first, which questions to ask, and second, how to quantify the answers in order to rank patients on a list — because ultimately, that's what needs to happen.

This is essentially the ancient philosophical problem of comparing the value of different lives, which modern medicine attempts to quantify through quality-adjusted life years (and it sounds like a joke). But poor formulation doesn't make the question disappear. First, you must decide what quality even means, then, whether every year counts the same, and finally, in which contexts these measurements apply. How do you compare the next five years of a child with leukemia to those of a thirty-year-old mother of two? I'm not trying to make anyone emotionally uncomfortable here. The point is to show how quickly these comparisons become far from straightforward, and how the line between clinical reasoning and moral judgment gets blurred.

What doesn't kill you – is probably a commensal

Nothing in biology makes sense except in the light of evolution.

- Theodosius Dobzhansky

When you work on mathematical modelling, you tend to resist the idea of assigning special attributes to certain parts of your system just for the sake of explaining the data. Special cases should emerge from the basic principles of the system, not because there is something inherently special about that case per se. By that logic, there are no special cases, there are only less and more frequent cases, both existing in the state space of your core mechanisms. If this is not the case, either a dimension is missing (which may feel like an inelegant way to resolve the issue, but sometimes it really is necessary), or there may exist a completely different set of mechanisms that would explain that (n+1)^th observation. Probably the best example is the transition from Newtonian to quantum mechanics: Quantum mechanics doesn't just extend Newtonian mechanics - it replaces its foundational assumptions.

Happy little local minimum

Everything is connected but some things are more connected than others. The world is a large matrix of interactions in which most of the entries are close to zero, and in which, by ordering those entries according to their orders of magnitude, a distinct hierarchic structure can be discerned.

- Herbert Simon

Lack of mechanistic interpretability in deep learning methods has been bothering engineers ever since the extensive deployment of neural networks in the 2010s. I actually find it hilarious how agitated we are by not understanding how they work and how we can't let it go 😅… Anyway, although this is purely about us not being able to interpret what they do (which is quite a human-centric problem), there is an unnatural feeling to how they work – and I don't use the word unnatural lightly here. For a long time I thought this was purely because of a key biological difference between our neurons and the so-called artificial neurons, following the debate about whether cognition is fundamentally computational¹. Turns out the unnaturalness might lie somewhere else entirely: not what learns, but the dynamism of what is being learnt.