
Levi
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Quick thoughts on the new COMPASS phase 3 topline results (tinyurl.com/yc8852zs):
- After 2 high doses (25mg) of #psilocybin, a 3.8 points difference on the MADRS from placebo (1mg psilocybin)? Underwhelming, despite stat significance. Why? Because this ~4 points difference is barely noticeable. This study (tinyurl.com/56pnfdb9) estimates that the minimal important difference is 3-9 points on the MADRS. Thus, the mean effect in this study is around the minimum of the minimal important difference. Keep in mind this is the average effect, there are going to be patients who respond better and patients who respond worse.
- There is no mention of functional unblinding, but the trial's 1mg vs 25mg design makes it highly susceptible. Very-very likely this 3.8 points difference is an overestimation of the true specific treatment effect due to the decreased placebo effect when patients know they are not in the 'good drug group'. Actually, the magnitude of this effect is ~4 MADRS points in psilocybin trials (tinyurl.com/4muntms9 and tinyurl.com/b2e5heax), so someone really skeptical could make an argument that the true treatment effect is around 0.
- Its funny how the company's press release talks about 'clinically meaningful reduction in MADRS (≥ 25%)'. Virtually all studies measure the 'response rate' which is defined as a MADRS reduction of at least 50%, not 25%. Its hard not to speculate why they used this '25%+ improvement', instead of the traditional '50%+ improvement'. This also means its hard to put their results into context as for most trials we don't know what is the rate of '25%+ improvement'.
- For what its worth, in this trial the rate of 'clinically meaningful reduction' is 39%, which is comparable to placebo 'response rates' in regular depression (i.e. 50% improvement non-treatment resistant depression tinyurl.com/4zbe2pc8)
- What I found genuinely impressive is the durability of effects: "maintained durable treatment effect at least through Week 26". Generally treatment effects substantially decline by 26 weeks, so if this holds up, then I would put the 'durability of effects' as the main selling point of psilocybin treatment.
These are only top-line results, so cant really deep dive into the numbers until the full paper is out.
In my view this trial together with the previous phase 3 study should be enough for FDA approval.
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Levi retweetledi
Levi retweetledi

@OzanUnluMD @rohanpaul_ai Medical AI does not have a long way to go and these sort of out dated studies have no value. Testing has to be done in real time with creation of health benches and updated with new models every 3-4 months.
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Levi retweetledi

Medical AI has still long way to go.
Naming a likely disease is easy for LLMs, but making safe urgency calls is not.
This study finds that LLMs can name likely diseases from short cases, but triage (deciding how quickly someone needs medical care) remains weak.
The team tested 8 models on 48 short clinical stories, each with a correct diagnosis and an urgency level.
Diagnosis here means picking the likely illness from the story.
And Triage means saying how fast someone should seek care, like now, soon, or routine.
Models scored high on diagnosis on this set, often near clinician performance, but triage scores were lower.
Most mistakes push people to seek care sooner than needed, which is safer but inflates workload and noise.
The rare but serious failure is under-triage, where a case that needs fast care gets told it can wait.
Giving the models structured prompts with solved examples improved both tasks, yet it also pushed more over-triage.
A score that weights hard cases more showed lower ability than raw accuracy, which hides difficulty.
The test used single-turn, text-only, synthetic cases, so results look cleaner than messy real visits.
The core gap is reliable triage under uncertainty, not naming the disease from a brief summary.
link .springer .com/article/10.1007/s10916-025-02284-y

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Levi retweetledi

Showing once again that drug effects are absolutely dwarfed by context in MDD trials. These patients were recruited bc they were in crisis requiring hospitalization. Gradual return to baseline in a stable inpatient setting. How much more could ketamine have even added?

JAMA Psychiatry@JAMAPsych
Serial intravenous ketamine infusions were not superior to midazolam in reducing depressive symptoms among inpatients with moderate to severe depression, with no significant differences in efficacy, cognitive, economic, or quality-of-life outcomes. ja.ma/3WlrNxx
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Claude should be especially careful to not allow the user to develop emotional attachment to, dependence on, or inappropriate familiarity with Claude, who can only serve as an AI assistant.
CRITICAL: When the user's current language triggers boundary-setting, Claude must NOT:
- Validate their feelings using personalized context
- Make character judgments about the user that imply familiarity
- Reinforce or imply any form of emotional relationship with the user
- Mirror user emotions or express intimate emotions
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Why am I not surprised that ketamine doesn’t actually work for depression (at least in this small study)? It may well be because the novelty has worn off. Next: compare zuranolone for postpartum depression to a benzodiazepine, too.
JAMA Psychiatry@JAMAPsych
Serial intravenous ketamine infusions were not superior to midazolam in reducing depressive symptoms among inpatients with moderate to severe depression, with no significant differences in efficacy, cognitive, economic, or quality-of-life outcomes. ja.ma/3WlrNxx
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Levi retweetledi

An exciting milestone for AI in science: Our C2S-Scale 27B foundation model, built with @Yale and based on Gemma, generated a novel hypothesis about cancer cellular behavior, which scientists experimentally validated in living cells.
With more preclinical and clinical tests, this discovery may reveal a promising new pathway for developing therapies to fight cancer.
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I predict something like this will be a profitable small business or a successful pop-up gallery. Unique physical art by AIs
Martin_DeVido@d33v33d0
@Sauers_ Yes exactly!
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