Towards a National System for Monitoring the Quality of Hospital-Based Stroke Services
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We sought to evaluate a system for monitoring the quality of hospital-based stroke services that uses routinely collected case fatality data, adjusted for case mix, as well as simple measures of the process of stroke care. METHODS: We compared the process of care and case fatality after stroke between 5 Scottish hospitals (A through E) during 1995-1997. We retrospectively identified 2724 patients with acute stroke using routine discharge data and obtained case mix and process of care data from the medical record. We ascertained case fatality by record linkage and adjusted for case mix using a simple, externally validated regression model. RESULTS: Crude case fatality varied by 21 deaths per 100 admissions between the 5 hospitals. After adjustment, case fatality still differed significantly (P=0.047), with 5 to 7 more deaths per 100 admissions at Hospital A than at Hospitals B through E. There were major shortcomings in the specialization and organization of care, the use of CT scanning, and the completeness of documentation at Hospital A compared with the other hospitals. There were smaller, but clinically important, differences in care between Hospitals B through E but no significant differences in adjusted case fatality. CONCLUSIONS: Once adjusted for important prognostic variables, routinely collected case fatality data might identify hospitals with major shortcomings in the processes of stroke care. More moderate, but still clinically important, variations in stroke care can only be identified by monitoring the process of care directly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".