Variation in initiating secondary prevention after myocardial infarction by hospitals and physicians, 1997 through 2004
Bibliographic record
Abstract
PURPOSE: Myocardial infarction (MI) survivors benefit from receiving secondary prevention, including beta-blockers, angiotensin-blocking agents, and statins, as recommended by guidelines. Compliance with these guidelines is suboptimal. We sought to describe the initiation of secondary prevention in MI survivors, and to describe the variation in initiation by discharging the hospital, the physician, and the physician "responsible" for secondary prevention prescribing decisions in British Columbia in 1997-2004. METHODS: We assembled a cohort of 28,613 patients discharged alive from the hospital after their first MI and were not readmitted within 30 days. Physicians responsible for prescribing post-MI secondary prevention medications were identified as the physicians prescribing the greatest number of cardiac medications (post-discharge cardiac prescribers). We used multilevel logistic regression to assess the variation in drug initiation at discharging hospital, discharging physician, and post-discharge cardiac prescriber levels, which were adjusted for patient and provider characteristics during the study period. RESULTS: Beta-blockers initiation increased from 56 to 71% over the 8-year study period; angiotensin-converting enzyme/angiotensin II receptor blocker initiation increased from 37 to 70%, and statin initiation increased from 22 to 66% (0-28% for high-potency statins). The probability for initiating an average patient with the study drugs varied widely in age-sex-adjusted models at the hospital and physician levels. Further adjustment did not meaningfully change findings. The variation was largest for statins. The maximum between-provider variance was found for high-potency statins in 2003-2004 at the post-discharge cardiac prescriber level. CONCLUSIONS: Study-drug initiation is increasing among MI survivors, but the variation in initiation is wide between discharging hospitals and physicians.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".