Effect of Hospital Closures on Acute Care Outcomes in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: In 2002 British Columbia, Canada began redistributing its hospital services. OBJECTIVE AND DESIGN: We used administrative data and interrupted time series analyses to determine how recent hospital closures affected patient outcomes. SUBJECTS: All adult acute myocardial infarction (AMI), stroke, and trauma events in British Columbia between fiscal years 1999 and 2013. Cases were patients whose closest hospital closed. Controls were matched by condition, year of event, and condition-specific hospital volume where treatment was received. MEASURES: Thirty-day mortality and hospital bypass rates. RESULTS: We matched 3267 AMI, 2852 stroke, and 6318 trauma cases to 1996, 1604, and 3640 controls, respectively. The 30-day mortality rate at baseline was 7.0% [95% confidence interval (CI), 4.0%-10.1%] for AMI, 5.3% (95% CI, 2.4%-8.1%) for stroke, and 1.2% (95% CI, 0.3%-2.1%) for trauma controls. The 30-day mortality rate for cases was 14.3% (95% CI, 7.1%-21.7%) for AMI, 12.0% (95% CI, 5.1%-18.9%) for stroke, and 3.1% for trauma (95% CI, 0.9%-5.2%) cases. There was no significant change in 30-day mortality for cases, and no significant difference in change in mortality rates between cases and controls following the intervention. The difference in hospital bypass rates between cases and controls was 50.1% (95% CI, 42.3%-57.9%) for AMI, 36.2% (95% CI, 27.4%-44.9%) for stroke, and 32.2% (95% CI, 27.7%-36.8%) for trauma cases preintervention. Following the intervention, the difference in bypass rates dropped by 15.5% (95% CI, 3.5%-27.5%) for AMI, 25.3% (95% CI, 11.7%-38.8%) for stroke, and 22.7% (95% CI, 15.7%-29.6%) for trauma cases. CONCLUSIONS: Hospital closures did not affect patient mortality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".