Ranking Hospitals According to Acute Myocardial Infarction Mortality
Bibliographic record
Abstract
OBJECTIVE: The objective of this population-based observational cohort study was to estimate the extent to which the inclusion/exclusion of transferred patients with acute myocardial infarction (AMI) impacts on hospital performance rankings. SUBJECTS: The authors studied 91,633 adult patients admitted to 116 acute care hospitals in Quebec, Canada, with a primary diagnosis of AMI between 1992 and 1999. MAIN OUTCOME MEASURE: Hospital performance ranks, based on 30-day AMI mortality rates, were estimated with hierarchical models and compared using 3 different methods for handling transferred patients (exclude all transfers; include transfers and assign outcome to the referring hospital; include transfers and assign outcome to the receiving hospital). The explanatory variable of interest was the hospital to which the patient's outcome was attributed. RESULTS: Using the 3 methods, 4 hospitals were ranked "best performers" once, and 1 hospital ranked among the best in 2 of the 3 analyses performed. Nine hospitals were ranked "worst performers" at least once (4 of which ranked among the "worst" once only, 2 ranked among the "worst" twice, and 3 were consistently ranked "worst performers" in all analyses). There was significant variation in mortality rates among hospitals, and the difference in the rates between the highest and lowest ranking hospitals exceeded the clinically relevant benchmark of 1%. CONCLUSIONS: Performance evaluation studies that compare hospital mortality rates typically exclude transferred patients. However, methods used to deal with AMI patient transfers influenced hospital ranks when comparing 30-day mortality rates. Excluding transfers may lead to an inaccurate depiction of the quality of healthcare services in regionalized healthcare systems that call for the timely interhospital transfer of patients with AMI.
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 | 0.000 | 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".