In‐hospital stroke: a multi‐centre prospective registry
Bibliographic record
Abstract
BACKGROUND: in-hospital strokes (IHS) are relatively frequent. Avoidable delays in neurological assessment have been demonstrated. We study the clinical characteristics, neurological care and mortality of IHS. METHODS: multi-centre 1-year prospective study of IHS in 13 hospitals. Demographic and clinical characteristics, admission diagnosis, quality of care, thrombolytic therapy and mortality were recorded. RESULTS: we included 273 IHS patients [156 men; 210 ischaemic strokes (IS), 37 transient ischaemic attacks (TIA) and 26 cerebral haemorrhages]. Mean age was 72 ± 12 years. Cardiac sources of embolism were present in 138 (50.5%), withdrawal of antithrombotic drugs in 77 (28%) and active cancers in 35 (12.8%). Cardioembolic stroke was the most common subtype of IS (50%). Reasons for admission were programmed or urgent surgery in 70 (25%), cardiac diseases in 50 (18%), TIA or stroke in 30 (11%) and other medical illnesses in 71 (26%). Fifty-two per cent of patients were evaluated by a neurologist within 3 h of stroke onset. Thirty-three patients received treatment with tPA (15.7%). Thirty-one patients (14.7%) could not be treated because of a delay in contacting the neurologist. During hospitalization, 50 patients (18.4%) died, 41 of them because of the stroke or its complications. CONCLUSIONS: cardioembolic IS was the most frequent subtype of stroke. Cardiac sources of embolism, active cancers and withdrawal of antithrombotic drugs constituted special risk factors for IHS. A significant proportion of patients were treated with thrombolysis. However, delays in contacting the neurologist excluded a similar proportion of patients from treatment. IHS mortality was high, mostly because of stroke.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".