Impact of an Expanded Hospital Recognition Program for Stroke Quality of Care
Bibliographic record
Abstract
BACKGROUND: In 2009, the Get With The Guidelines-Stroke (GWTG-Stroke) program offered additional recognition if hospitals performed well on certain stroke quality measures. We sought to determine whether quality of care for all hospitals participating in GWTG-Stroke improved with this expanded recognition program. METHODS AND RESULTS: We examined hospital-level performance on 6 quality of care (process) measures and 1 defect-free composite quality measure for stroke following expansion of the existing performance measure recognition program. Compliance with all measures improved following launch of the expanded program, and this rate increased significantly for all 9 measures. When evaluated as the relative rate of increase in use over time, process improvement slowed significantly (P<0.05) following launch of the program for 2 measures, and accelerated significantly for 1 measure. However, when evaluated as a gap in care, the decrease in the quality gap was greater following launch of the program for 5 of 6 (83%) measures. There was no evidence that other processes of stroke care suffered as the result of the increase in measures and expanded recognition program. CONCLUSIONS: While care for stroke continues to improve in this country, expanded hospital process performance recognition had mixed results in accelerating this improvement. However, the quality gap continues to shrink among those participating in provider performance programs.
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 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".