Effect of a provincial system of stroke care delivery on stroke care and outcomes
Bibliographic record
Abstract
BACKGROUND: Systems of stroke care delivery have been promoted as a means of improving the quality of stroke care, but little is known about their effectiveness. We assessed the effect of the Ontario Stroke System, a province-wide strategy of regionalized stroke care delivery, on stroke care and outcomes in Ontario, Canada. METHODS: We used population-based provincial administrative databases to identify all emergency department visits and hospital admissions for acute stroke and transient ischemic attack from Jan. 1, 2001, to Dec. 31, 2010. Using piecewise regression analyses, we assessed the effect of the full implementation of the Ontario Stroke System in 2005 on the proportion of patients who received care at stroke centres, and on rates of discharge to long-term care facilities and 30-day mortality after stroke. RESULTS: We included 243 287 visits by patients with acute stroke or transient ischemic attack. The full implementation of the Ontario Stroke System in 2005 was associated with an increase in rates of care at stroke centres (before implementation: 40.0%; after implementation: 46.5%), decreased rates of discharge to long-term care facilities (before implementation: 16.9%; after implementation: 14.8%) and decreased 30-day mortality for hemorrhagic (before implementation: 38.3%; after implementation: 34.4%) and ischemic stroke (before implementation: 16.3%; after implementation: 15.7%). The system's implementation was also associated with marked increases in the proportion of patients who received neuroimaging, thrombolytic therapy, care in a stroke unit and antithrombotic therapy. INTERPRETATION: The implementation of an organized system of stroke care delivery was associated with improved processes of care and outcomes after stroke.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".