Admission to hospital versus non-admission after stroke:Trends and survival using the South London Stroke Register
Bibliographic record
Abstract
Background: To identify trends and differences in survival between patients admitted to hospital versus non admission after first ever stroke.<br/><br/>Methods: Population based stroke register of first in a life time strokes between 1995 and 2012 were examined. Baseline data were collection of socio-demographic factors, stroke subtype, case mix and risk factors before stroke. Survival curves were estimated with Kaplan-Meier methods.<br/><br/>Results: 3464 patients were admitted to hospital for stroke. 458 patients were managed in the community. Patients admitted to hospital were more likely to be younger (P=0.02), have more severe impairments for stroke: coma, dysphagia, incontinence, haemorrhagic stroke (P<0.001) and atrial fibrillation (P=0.001). There was a significant trend for increasing admission across subsequent cohorts, 1995-2000 (83%), 2001-2006 (90%) and 2007-2012 (94%), P<0.001. Median (months) survival was higher for non admission (36 vs. 79), P<0.0001 and case fatality at 90 days was lower for non admission (24% vs. 2.6%), P<0.0001. When survival analysis was stratified according to Barthel ≥ 15 at day 7, there no significant differences in survival curves between both groups in 1995-2000 (P=0.5) or 2001-2006 (P=0.4) but there was a significant trend for higher survival rates for non admission in the 2007-2012 cohort (P=0.02).<br/><br/>Conclusion: There is a significant trend for increasing hospital admission over time. There appears to be a survival advantage in the latter cohort for those patients with lower levels of clinical disability at day 7 who are not admitted which requires further explanation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".