Rural versus urban academic hospital mortality following stroke in Canada
Bibliographic record
Abstract
INTRODUCTION: Stroke is one of the leading causes of death in Canada. While stroke care has improved dramatically over the last decade, outcomes following stroke among patients treated in rural hospitals have not yet been reported in Canada. OBJECTIVES: To describe variation in 30-day post-stroke in-hospital mortality rates between rural and urban academic hospitals in Canada. We also examined 24/7 in-hospital access to CT scanners and selected services in rural hospitals. MATERIALS AND METHODS: We included Canadian Institute for Health Information (CIHI) data on adjusted 30-day in-hospital mortality following stroke from 2007 to 2011 for all acute care hospitals in Canada excluding Quebec and the Territories. We categorized rural hospitals as those located in rural small towns providing 24/7 emergency physician coverage with inpatient beds. Urban hospitals were academic centres designated as Level 1 or 2 trauma centres. We computed descriptive data on local access to a CT scanner and other services and compared mean 30-day adjusted post-stroke mortality rates for rural and urban hospitals to the overall Canadian rate. RESULTS: A total of 286 rural hospitals (3.4 million emergency department (ED) visits/year) and 24 urban hospitals (1.5 million ED visits/year) met inclusion criteria. From 2007 to 2011, 30-day in-hospital mortality rates following stroke were significantly higher in rural than in urban hospitals and higher than the Canadian average for every year except 2008 (rural average range = 18.26 to 21.04 and urban average range = 14.11 to 16.78). Only 11% of rural hospitals had a CT-scanner, 1% had MRI, 21% had in-hospital ICU, 94% had laboratory and 92% had basic x-ray facilities. CONCLUSION: Rural hospitals in Canada had higher 30-day in-hospital mortality rates following stroke than urban academic hospitals and the Canadian average. Rural hospitals also have very limited local access to CT scanners and ICUs. These rural/urban discrepancies are cause for concern in the context of Canada's universal health care system.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".