Hospital volume and stroke outcome
Bibliographic record
Abstract
BACKGROUND: Although hospital-outcome relationships have been explored for a variety of procedures and interventions, little is known about the association between annual stroke admission volumes and stroke mortality. Our aim was to determine whether facility type and hospital volume was associated with stroke mortality. METHODS: All hospital admissions for ischemic stroke were identified from the Hospital Morbidity database (HMDB) from April 2003 to March 2004. The HMDB is a national database that contains patient-level sociodemographic, diagnostic, procedural, and administrative information across Canada. Ischemic stroke was identified through patient's principal diagnosis recorded using the International Classification of Diseases (9 and 10). Multivariable analysis was performed with generalized estimating equations with adjustment for demographic characteristics, provider specialty, facility type, hospital volume, and clustering of observations at institutions. RESULTS: Overall, 26,676 patients with ischemic stroke were admitted to 606 hospitals. Seven-day stroke mortality was 7.6% and mortality at discharge was 15.6%. Adverse outcomes were more frequent in patients treated in low-volume facilities (<50 strokes/year) than in those treated in high volume facilities (100 to 199 and >200 strokes patients/year) (for 7-day mortality: 9.5 vs 7.3%, p < 0.001; 9.5 vs 6.0%, p < 0.001; for discharge mortality: 18.2 vs 15.2%, p < 0.001; 18.2 vs 12.8%, p < 0.001). The difference persisted after multivariable adjustment or when hospital volume was divided into quartiles. CONCLUSIONS: High annual hospital volume was consistently associated with lower stroke mortality. Our study encourages further research to determine whether this is due to differences in case mix, more organized care in high-volume facilities, or differences in the performance or in the processes of care among facilities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".