A Comprehensive Secondary Prevention Benchmark (2PBM) Score Identifying Differences in Secondary Prevention Care in Patients After Acute Coronary Syndrome
Bibliographic record
Abstract
PURPOSE: The objective of this study was to quantify secondary prevention care by creating a secondary prevention benchmark (2PBM) score for patients undergoing ambulatory cardiac rehabilitation (CR) after acute coronary syndrome (ACS). METHODS: In this observational cohort study, 472 consecutive ACS patients who completed the ambulatory CR program between 2017 and 2019 were included. Benchmarks for secondary prevention medication and clinical and lifestyle targets were predefined and combined in the comprehensive 2PBM score with maximum 10 points. The association of patient characteristics and achievement rates of components and the 2PBM were assessed using multivariable logistic regression analysis. RESULTS: Patients were on average 62 ± 11 yr of age and predominantly male (n = 406; 86%). The types of ACS were ST-elevation myocardial infarction (STEMI) in 241 patients (51%) and non-ST-elevation myocardial infarction in 216 patients (46%). Achievement rates for components of the 2PBM were 71% for medication, 35% for clinical benchmark, and 61% for lifestyle benchmark. Achievement of medication benchmark was associated with younger age (OR = 0.979: 95% CI, 0.959-0.996, P = .021), STEMI (OR = 2.05: 95% CI, 1.35-3.12, P = .001), and clinical benchmark (OR = 1.80: 95% CI, 1.15-2.88, P = .011). Overall ≥8 of 10 points were reached by 77% and complete 2PBM by 16%, which was independently associated with STEMI (OR = 1.79: 95% CI, 1.06-3.08, P = .032). CONCLUSIONS: Benchmarking with 2PBM identifies gaps and achievements in secondary prevention care. ST-elevation myocardial infarction was associated with the highest 2PBM scores, suggesting best secondary prevention care in patients after ST-elevation myocardial infarction.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".