Canadian Stroke Quality of Care Study: Identification of performance indicators for acute stroke care
Bibliographic record
Abstract
Background: Initiatives including regionalization of stroke care, clinical practice guideline development and quality-of-care improvement efforts are best supported using clearly defined performance measures and accurate, high-quality data sources. In Canada, there are no published consensus statements or guidelines, and consensus-based indicators to evaluate the quality of stroke care are lacking. We sought to identify a core set of quality-of-care indicators to evaluate acute ischemic stroke care in hospitals. Methods: A Canadian expert panel was convened to select key quality-of-care measures for acute stroke using a modified Delphi process. A list of 51 potential quality-of-care indicators was compiled from 44 indicators chosen in a previous US study and others identified from a review of current stroke literature including practice guidelines, randomized trials, systematic reviews and observational studies of stroke care bestpractice; the list was accompanied by a summary of available evidence supporting each indicator. Panel members rated each indicator using 6 dimensions of quality and then met to discuss the overall value of, levels of evidence for, and reliability of using each indicator to measure acute stroke care. Results: On the basis of panel discussions and recommendations, 24 of the 51 potential indicators were discarded or combined with other indicators. Of the final 27 potential indicators, 23 were strongly supported by the majority of panel members and underwent further development and measurement. Of these 23 indicators, 16 were also strongly supported by at least 75% of panelists in the previous US study. Interpretation: A core set of Canadian quality-of-care indicators for monitoring acute ischemic stroke care is now available. These indicators present a foundation for stroke care improvement initiatives and for ongoing research in acute stroke management.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".