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#clinical — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #clinical, aggregated by home.social.

  1. I stand with #NHS midwives

    Across our NHS, #midwives are routinely working shifts without rest breaks.

    - Many are signed up to opt out of the #Working Time #Regulations, not freely, but because speaking up feels impossible.

    - #Clinical #errors linked to #fatigue are not rare #accidents, they are what happens when a system asks too much of too few people for too long.

    Sign:

    the.organise.network/campaigns

    #SaveOurNHS #ToxicLabour #ToxicReform #ToxicTories #WorkHours

  2. For once, an #AI issue where I *can* speak with a certain amount of authority.

    I've been hearing "expert systems outperform human #diagnostics, so pretty soon human #physicians will be obsolete" for a few years now. It's closely akin to "#airliners fly themselves these days, so what do we need #pilots for?" In both cases, people are paying attention to the best-case scenario with no understanding of the *enormous* number of how many and various the worse cases really are.

    This is complicated by the fact that in most of #medicine (although not necessarily #emergency medicine, the author's specialty) and in nearly all of air travel, the best case is also the normal case. Most of the time, whatever is wrong with you can be diagnosed and treated. Almost all the time, when you get on a plane, you'll walk off at the other end of the trip as healthy as when you boarded. It's reasonable to expect those outcomes.

    Not-best and not-normal cases add up really fast.

    Without any false modesty whatsoever: as a #medic, I learned a truly impressive degree of clinical judgement. From the first moment I saw a patient, I had a pretty good idea of #diagnosis, #treatment, and #prognosis. (Sadly, if my initial call was "this one's not going to make it," I was almost always right. The exceptions kept me going.) I learned from the best—one of my mentors had learned *his* trade in rural Guatemala, where resources were terribly sparse and human judgement was the only line between life and death. He held back death for decades, and it came for him far too early. Gene Gibbs, RIP.

    I can't code that. Neither can anyone else, and if they tell you they can, they're lying.

    As a #researcher, I've done a fair amount of work in #clinical #decision #support (#CDS). The idea is simple, and valid: no human, or team of humans, can remember everything they need to know. There's simply too much knowledge for the brain to hold and recall on demand. Subtle relationships exist between disparate types of data that *nobody* knows, until we tease out the numbers. We're doing this, right now. It is saving lives and relieving suffering, right now.

    The key word there is "support." Humans still absolutely, positively, 100% need to be in the loop.

    Maybe that will change, someday. I'm not saying it's impossible, for two reasons. First, any time anyone says "computers will never be able to ___" they're usually proven wrong. Second, I don't want to limit my and my colleagues' imaginations. We need to stay focused, but it is a *good thing* for our reach to slightly exceed our grasp. That's how #science happens!

    Just not this day, and not for many days to come. Right now, we need to keep muddling along. There's not much more human than that.

    fastcompany.com/90863983/chatg

  3. #Deer #antlers, which are bone, #regenerate each year and can grow at a rate of up to 2.75 cm/day!

    Qin et al (2023) studied this and found that the #molecular machinery behind this phenomenon is also found in other #mammals (like #mice), but not in the non-mammals that they tested.

    This may have #clinical implications for developing new #bone #growth #research and #therapies (with caveats, of course).

    🦌 🐀 🦌 🐀 🦌 🐀

    Explainer: science.org/doi/10.1126/scienc

    Paper: science.org/doi/full/10.1126/s

  4. Ever since the Human #Genome Project got rolling about thirty years ago (!) there’s been a lot of hope, and a lot of hype, about “#personalized #medicine” or “#precision medicine.” When it became clear that as always, the results weren’t going to match the hype, a lot of the hope went away too. This is a mistake.

    I’d like to talk about a quiet revolution in precision medicine: #genetic #dosage guidelines, a.k.a. #pharmacogenomic #labeling. The basic idea is that if you carry certain genetic #variants, you may need considerably more or less of a particular medication than the standard dose. Back in the ’90s, the kind of genetic #analysis needed to make use of that information was far too expensive and time-consuming for #clinical practice. These days you can get a complete #sequence in a matter of hours, for the same cost as a battery of standard blood tests.

    Fifteen years ago or so, the FDA approved the first pharmacogenomic labeling, for #warfarin. I was lucky enough to be in the room when the researchers made the announcement, and you could have heard a pin drop. Now it’s routine, and there’s a very long list: fda.gov/drugs/science-and-rese

    Everyone reacts to #medications differently. For most patients, most medications, and most diseases, there’s a fairly broad range of clinical effectiveness between “too little to do any good” and “way too much.” But for a substantial number of all of the above, the range is much narrower—and when you add up all the special cases, you get a hell of a lot of people!

    A lot of #drugs never get approved, despite showing promise in clinical #trials, because they only help a portion of the study population. Regulatory bodies like the #FDA are notoriously resistant to #subgroup analysis, and I get why: it’s very easy to cherry-pick those subjects in a clinical trial who happen to do well, and then come up with a post hoc explanation for why the test treatment worked for them but not for other participants. Some bad drugs have made it to market because of this kind of chicanery. But of course sometimes there’s a real reason one group does better, and as long as genetic testing is part of the study design from the start, it’s becoming possible to convince regulators that reason is valid.

    My work is mostly upstream of this, in the drug #target #discovery phase: finding disease-related #genes and #proteins that might be modifiable with the right medication. Since it’s part of the project from the start, that makes trial design easier, and the results more likely to be accepted. But I’d really like to see more #genomic analysis on drugs that aren’t designed that way too, and I think we’re getting there.

    Genetic dosage guidelines, though, are making a real difference in current practice. There are still considerable debates over the merits of many labelings, driven partly by legitimate #statistical concerns and partly by ideology. But the principle is proven beyond reasonable doubt, and it’s saving lives and relieving suffering right now, every day. Much more to come.