The Impact of Unmeasured Clinical Variables on the Accuracy of Hospital Report Cards: A Monte Carlo Study
Bibliographic record
Abstract
PURPOSE: Hospital report cards are commonly produced using administrative data. The objective of this study was to determine the impact of unmeasured clinical data on the accuracy of hospitals' report cards. METHODS: Monte Carlo simulations were based on both administrative and detailed clinical data for patients hospitalized with an acute myocardial infarction in Ontario, Canada. Data were simulated such that the true performance of each hospital was known. Both clinical and administrative risk scores were randomly generated for each patient. The ability of hospital report cards to correctly identify hospitals that truly had higher than acceptable mortality was compared when both clinical and administrative data were used and when only administrative data were used. By using Monte Carlo simulations, we were able to incrementally increase the divergence between the 2 risk scores. RESULTS: In a wide range of settings, sensitivity and specificity of hospital report cards was only negligibly greater when both administrative and clinical data were used compared to when only administrative data were used. CONCLUSIONS: Unmeasured clinical data have at most a minor impact on the accuracy of cardiac hospital report cards.
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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 | 0.081 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".