Stroke Quality Metrics
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Stroke quality metrics play an increasingly important role in quality improvement and policies related to provider reimbursement, accreditation, and public reporting. We conducted 2 systematic reviews examining the relationships between compliance with stroke quality metrics and patient-centered outcomes, and public reporting of stroke metrics and quality improvement, quality of care, or outcomes. METHODS: MEDLINE and EMBASE databases were searched to identify studies that evaluated the relationship between stroke quality metric compliance and patient-centered outcomes in acute hospital settings and public reporting of stroke quality metrics and quality improvement activities, quality of care, or patient outcomes. We specifically excluded studies that evaluated the effect of stroke units or hospital certification. RESULTS: Fourteen studies met eligibility criteria for the review of stroke quality metric compliance and patient-centered outcomes; 9 found mostly positive associations, whereas 5 found no or very limited associations. Only 2 eligible studies were found that directly addressed the public reporting of stroke quality metrics. CONCLUSIONS: Some studies have found positive associations between stroke metric compliance and improved patient-centered outcomes. However, high-quality studies are lacking and several methodological difficulties make the interpretation of the reported associations challenging. Information on the impact of public reporting of stroke quality metric data is extremely limited. Legitimate questions remain as to whether public reporting of stroke metrics is accurate, effective, or has the potential for unintended consequences. The generation of high-quality data examining quality metrics and stroke outcomes as well as the impact of public reporting should be given priority.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".