Failure-to-Rescue After Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Failure-to-rescue (FTR), originally developed to study quality of care in surgery, measures an institution's ability to prevent death after a patient becomes complicated. OBJECTIVES: Develop an FTR metric modified to analyze acute myocardial infarction (AMI) outcomes. RESEARCH DESIGN: Split-sample design: a random 20% of hospitals to develop FTR definitions, a second 20% to validate test characteristics, and an out-of-sample 60% to validate results. SUBJECTS: Older Medicare beneficiaries admitted to short-term acute-care hospitals for AMI between 2009 and 2011. MEASURES: Thirty-day mortality and FTR rates, and in-hospital complication rates. RESULTS: The 60% out-of-sample validation included 234,277 patients across 1142 hospitals that admitted at least 50 patients over 2.5 years. In total, 72.1% of patients were defined as Medically Complicated (complex on admission or subsequently developed a complication or died without a recorded complication) of whom 19.3% died. Spearman r between hospital risk-adjusted 30-day mortality and FTR was 0.89 (P<0.0001); Mortality versus Complication=-0.01 (P=0.6198); FTR versus Complication=-0.10 (P=0.0011). Major teaching hospitals displayed 19% lower odds of FTR versus non-teaching hospitals (odds ratio=0.81, P<0.0001), while hospitals as a group defined by teaching hospital status, comprehensive cardiac technology, and having good nursing mix and staffing, displayed a 33% lower odds of FTR (odds ratio=0.67, P<0.0001) versus hospitals without any of these characteristics. CONCLUSIONS: A modified FTR metric can be created that has many of the advantageous properties of surgical FTR and can aid in studying the quality of care of AMI admissions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".