Benchmarks for acute stroke care delivery
Bibliographic record
Abstract
OBJECTIVE: Despite widespread interest in many jurisdictions in monitoring and improving the quality of stroke care delivery, benchmarks for most stroke performance indicators have not been established. The objective of this study was to develop data-derived benchmarks for acute stroke quality indicators. DESIGN: Nine key acute stroke quality indicators were selected from the Canadian Stroke Best Practice Performance Measures Manual. PARTICIPANTS: A population-based retrospective sample of patients discharged from 142 hospitals in Ontario, Canada, between 1 April 2008 and 31 March 2009 (N = 3191) was used to calculate hospital rates of performance and benchmarks. INTERVENTION: The Achievable Benchmark of Care (ABC™) methodology was used to create benchmarks based on the performance of the upper 15% of patients in the top-performing hospitals. MAIN OUTCOME MEASURES: Benchmarks were calculated for rates of neuroimaging, carotid imaging, stroke unit admission, dysphasia screening and administration of stroke-related medications. RESULTS: The following benchmarks were derived: neuroimaging within 24 h, 98%; admission to a stroke unit, 77%; thrombolysis among patients arriving within 2.5 h, 59%; carotid imaging, 93%; dysphagia screening, 88%; antithrombotic therapy, 98%; anticoagulation for atrial fibrillation, 94%; antihypertensive therapy, 92% and lipid-lowering therapy, 77%. ABC™ acute stroke care benchmarks achieve or exceed the consensus-based targets required by Accreditation Canada, with the exception of dysphagia screening. CONCLUSIONS: Benchmarks for nine hospital-based acute stroke care quality indicators have been established. These can be used in the development of standards for quality improvement initiatives.
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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 | 0.044 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".