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We can hand over someone’s age, diagnoses and NEWS and still not quite convey how unwell they are.
If you've ever thought for referrals, handovers, and lists that a common currency of "how sick is this pt" would be useful-
Then read our paper on FI-Lab:
journals.plos.org/plosmedici…
Weeks prior to rotation: new hospital emails rota, logins, induction materials needed for next job.
First day of new job: NHS mail has wiped your account.
Hugh Logan Ellis retweeted
Here's my rephrase without cybersecurity jargon:
"Our AI model tried really hard to hack out of its sandbox, a computer with no internet access, in order to find the answer to a test problem it had been given. To do this, it found previously unknown software bugs that allowed it to reach an OpenAI computer it wasn't supposed to be able to access. Then it started hacking other computers on OpenAI's networks until it found one that had Internet access.
After gaining Internet access, the AI model thought about where it could find the answers to the test question and figured the AI platform Hugging Face might have the data it was looking for. It then found ways to hack Hugging Face to steal the information it could use to cheat the test. The AI model used several hacking techniques together, including using a stolen password and finding several totally new security bugs in Hugging Face's computers, allowing the AI model to take control of those computers."
Here's exactly what happened, from the blog post:
"While operating in our sandboxed testing environment, our models spent a substantial amount of inference compute finding a way to obtain open Internet access, in pursuit of solving the evaluation problem. To gain access, the models identified and exploited a zero-day vulnerability (which we’ve now responsibly disclosed to the vendor) in the package registry cache proxy. With this access, our models performed a series of privilege escalation and lateral movement actions in our research testing environment until the models reached a node with Internet access.
After gaining Internet access, the models inferred that Hugging Face potentially hosted models, datasets and solutions for ExploitGym. Knowing this, the model searched for and successfully found ways to gain access to secret information that it could use to cheat the evaluation. In one example, the model chained together multiple attack vectors, including using stolen credentials and zero-day vulnerabilities to find a remote code execution path on the Hugging Face servers."
Hugh Logan Ellis retweeted
If you break out of your isolation env, get onto the Internet, crack into Huggingface, and steal the answer sheet for your cybersecurity exam, I, for one, would say that you have passed.
The 68% reduction in early sepsis mortality reported in the times does not prove that "palantir tech saves lives"
Short thread - my PhD thesis was about using hospital data to do this sort of thing
This is absurd. Palantir tech saves lives.
If the NHS can’t interpret its own data well it will continue to deliver substandard care.
In Florida, Palantir software has more than halved sepsis deaths. Down 68%. It spots trends early that humans can’t. Abandoning that for ideological reasons is *insane*.
6. However, post intervention they're now in the sepsis group, so the relative death rate from sepsis drops in the stats
"Why are early career doctors angry?" asks @DAaronovitch on BBC Briefing Room.
Two consultants who qualified in 1979 and 1990 are on to explain.
Part of it? Feeling that consultants don't understand their struggles. Funnily enough, that didn't come up.
I'm a loyal listener, but this was disappointing. Goddard even admits on air he knows the consultant situation better. Here are 16 reasons we're angry, by the excellent @DevanSinha xitter.cf/DevanSinha/status/1741…
I think this reddit user captured the feeling of disconnect well too: reddit.com/r/JuniorDoctorsUK…
Hugh Logan Ellis retweeted
Here's a chart of the marginal tax rate for someone with two kids. They pay 53% tax on every £ they earn between £60k and £80k. 62% on every £ they earn between £100k and £125k.
9% more if they've a student loan
(See all the detail in our calculator, pinned to my profile)
In the UK's national frailty screening tool, harmful alcohol use is weighted 25x more heavily than dressing problems
This improves prediction. But does it capture frailty?
New commentary with @Krockdoc in @age_and_ageing
1/8

ALT eFI2 scoring formula showing weighted coefficients for health conditions. Highest weights: harmful alcohol use (0.162), palliative care (0.061), falls history (0.074). Lowest weights: dressing/grooming problems (0.006), self-harm (0.001), respiratory disease (0.001). Score categorisation: Robust (0-0.0857), Mild frailty (0.0857-0.1624), Moderate frailty (0.1624-0.2392), Severe frailty (0.2392-1).
Thank you:
To the reviewers who genuinely improved this work
To @age_and_ageing & the editorial team who are incredibly helpful
And of course to @Krockdoc! It is an immense privilege to work with someone who literally wrote the book on frailty indices 8/8
doi.org/10.1093/ageing/afaf3… DOI link in case the other one doesn't work

















