'Doctor, I'm worried about this patient's observations.' We find them bolt upright, gripping the rails, wheezing with each gasp. RR & BP up, sats down. Classic APO - we got here just in time. Yet an AI for detecting deterioration might have missed this. Our paper explains why.

I have written up my thoughts on "Physician Associate graduates have comparable knowledge to medical graduates" here on reddit (where I can do things like edit):. *I strongly disagree with any comments criticising the authors personally*.

RT @Doc_HLE: Do Clinical Frailty Scores (CFS) measured in A&E tell us anything useful?. Tiny answer: Yes. 1/7.

Thank you to to the patients, clinical teams & data collectors (esp. the ED nurses!) my co-authors @Krockdoc @jthteo @IbrahimZina @rutheyres1, @liamdunnell peer reviewers and editorial team at @Age_and_Ageing.

Long answer? Please do read the full paper, it's open access in @Age_and_Ageing . 7/7.

Takeaway? CFS strongly predicts outcomes. Recognising the variability we observed (worth checking your own ED data?) is key to refining its application. We think automated tools could offer useful support here. 6/7.

It also seemed why you were in A&E played a role. Arrive confused or acutely unwell? You might be rated frailer than someone with a less alarming issue. Understandable, perhaps, but it does suggest the score might be reflecting more than just baseline frailty. 5/7.

The 'But'. reliability. Well, that's where it gets interesting. We saw scores for the same patient varying rather dramatically between visits, from 'Fit' to 'Severely Frail' to 'Fit' again. These results don't fit with our understanding of frailty. 4/7

The 'Yes' part: Those CFS scores do correlate strongly with important things - hospital admission, length of stay, short, medium and long-term survival. They're clearly picking up on something significant for patients. 3/7

Short answer: Yes, but. it's complicated:. While CFS screening became wider NHS guidance in 2019, @KingsCollegeNHS EDs started way back in 2017 giving us a over 68,000 scores to analyse; the largest cohort of CFS scores in the literature. 2/7.

Do Clinical Frailty Scores (CFS) measured in A&E tell us anything useful?. Tiny answer: Yes. 1/7.

And finally - anonymous Reviewer 2, whoever you are - thank you (yes, really!). Your thoughtful feedback and the Associate Editor's guidance significantly strengthened this paper.

Huge thanks to @IbrahimZina for encouraging me to write this up, @DocEd for helping frame it in the language of causal inference, @mbwhyte1 for shaping how I think about and articulate clinical problems, and @jthteo & @Krockdoc for supervision and bulking out those COIs!.

What's the solution? How do we build AI systems that can spot these cases when successful treatment makes them invisible in our data? You'll need to read the paper in @npjDigitalMed for that.

These are exactly the patients we want to catch - where timely treatment prevents a crisis. Where prompt intervention turns someone from fighting for breath to sipping tea within the hour.

Here's the problem though: Because we treated our APO patient successfully, they didn't die or need ICU. To an AI learning from historical data, this looks like a 'false alarm.' But it wasn't false at all - it was a life saved through early intervention.

But our current systems aren't perfect - sometimes we do miss deteriorating patients. That's why researchers are turning to AI, using historical patient records to spot patterns in cases where patients died or needed intensive care. The goal? To catch deterioration earlier.

What happened here? Flash pulmonary oedema - fluid rapidly filled their lungs. But with prompt treatment (GTN, O2, diuretics), we can fix this. Our traditional warning system saw the obs, and triggered a review. A nurse recognised something wrong and called. They recovered.

@jthteo Seriously, if you knew how many attempts this took to get right. It's embarrassing. Why no edit button?.

@jthteo If only he could help me with my tweet impediment. .