Does the Volume of Ischemic Stroke Admissions Relate to Clinical Outcomes in the Ontario Stroke System?
Bibliographic record
Abstract
BACKGROUND: Better outcomes have been found among hospitals treating higher volumes of patients for specific surgical and medical conditions. We examined hospital ischemic stroke (IS) volume and 30-day mortality to inform regionalization planning. METHODS AND RESULTS: Using a population-based hospital discharge administrative database (2005/2006 to 2011/2012), average annual IS patient volumes were calculated for 162 Ontario acute hospitals. Hospitals were ranked and classified as small (<126), medium (126-202), and large (>202). Hierarchical multivariable logistic regression was used to estimate the odds of death within 7 and 30 days to account for the homogeneity in outcomes for patients treated at the same hospital. Overall, 73 368 patients were hospitalized for IS, and 30-day mortality was 15.3%. The mean (±SD) of annual hospitalizations for IS was 29 (31) for small-volume hospitals, 156 (20) for medium-volume hospitals, and 300 (78) for high-volume hospitals. High-volume hospitals admitted younger patients (mean [±SD] age, 73.0 [13.9] years) compared with medium- and small-volume hospitals (74.0 [13.2] and 75.5 [12.5] years, respectively; P<0.0001). Patients at small-volume hospitals were more likely than patients at high-volume hospitals to die at 30 days after an acute IS (adjusted odds ratio, 1.37; 95% confidence interval, 1.14-1.65). CONCLUSIONS: Hospital IS volume is associated with 30-day mortality in Ontario. Patients admitted to hospitals with annual IS volumes <126 annually are more likely to die within 30 days than patients admitted to hospitals that see on average 300 patients annually. This finding supports centralizing care in stroke-specialized hospitals.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".