Estimating the Net Benefit of Improvements in Hospital Performance
Bibliographic record
Abstract
BACKGROUND: It is important to be able to estimate the anticipated net population benefit if the performance of hospitals is improved to specific standards. OBJECTIVE: The objective of this study was to show how G-computation can be used with random effects logistic regression models to estimate the absolute reduction in the number of adverse events if the performance of some hospitals within a region was improved to meet specific standards. RESEARCH DESIGN: A retrospective cohort study using health care administrative data. SUBJECTS: Patients hospitalized with acute myocardial infarction in the province of Ontario in 2015. RESULTS: Of 18,067 patients hospitalized at 97 hospitals, 1441 (8.0%) died within 30 days of hospital admission. If the performance of the 25% of hospitals with the worst performance had their performance changed to equal that of the 75th percentile of hospital performance, 3.5 deaths within 30 days would be avoided [95% confidence interval (CI): 0.4-26.5]. If the performance of those hospitals whose performance was worse than that of an average hospital had their performance changed to that of an average hospital, 6.0 deaths would be avoided (95% CI: 0.7-47.0). If the performance of the 75% of hospitals with the worst performance had their performance changed to equal that of the 25th percentile of hospital performance, 11.0 deaths would be avoided (95% CI: 1.2-79.0). CONCLUSION: G-computation can be used to estimate the net population reduction in the number of adverse events if the performance of hospitals was improved to specific standards.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".