Development of Stroke Performance Measures
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In the United States and elsewhere, stroke performance measures have been developed to monitor and improve the quality of care. The process by which these measures are developed, implemented, and evaluated is complex, evolving, and not widely understood. We review the methodological development of stroke performance measures in the United States. METHODS: A literature search identified articles that addressed the development and endorsement of performance measures for stroke care. Emphasis was given to articles specific to acute stroke, but when these were lacking, other cardiovascular diseases were included. RESULTS: Ten process-based performance measures relevant to acute hospital-based stroke care have now been developed and endorsed. These measures include intravenous thrombolysis, deep vein thrombosis prophylaxis, dysphagia screening, stroke education, and discharge-related medications and assessments. There are currently at least 5 major US-based stroke quality improvement programs implementing stroke measures. Data indicate that rapid improvements in the quality of stroke care can be induced by the systematic collection and evaluation of stroke performance measures. However, current stroke measures are relatively limited, addressing only inpatient care and mostly patients with ischemic stroke. CONCLUSIONS: Stroke quality improvement is still in its early stages, but data suggest that large-scale improvements in stroke care can result from the implementation of stroke performance measures. Performance measures that address multidisciplinary stroke unit care, outpatient-based care, and patient-oriented outcomes such as functional recovery should be considered. Ongoing challenges relevant to stroke quality improvement include the role of public reporting and the need to link better stroke care to improved patient outcomes.
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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 | 0.177 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".