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. 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. 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). 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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".