Factors associated with the use of evidence-based therapies after discharge among elderly patients with myocardial infarction
Bibliographic record
Abstract
BACKGROUND: In an accompanying article, we report moderate between-hospital variation in the postdischarge use of beta-blockers, angiotensin-modifying drugs and statins by elderly patients who had been admitted to hospital with acute myocardial infarction. Our objective was to identify the characteristics of patients, physicians, hospitals and communities associated with differences in the use of these medications after discharge. METHODS: For this retrospective, population-based cohort study, we used linked administrative databases. We examined data for all patients aged 65 years or older who were discharged from hospital in 2005/06 with a diagnosis of myocardial infarction. We determined the effect of patient, physician, hospital and community characteristics on the rate of postdischarge medication use. RESULTS: Increasing patient age was associated with lower postdischarge use of medications. The odds ratios (ORs) for a 1-year increase in age were 0.98 (95% confidence interval [CI] 0.97-0.99) for beta-blockers, 0.97 (95% CI 0.97-0.98) for angiotensin-converting-enzyme inhibitors and angiotensin-receptor blockers and 0.94 (95% CI 0.93-0.95) for statins. Having a general or family practitioner, a general internist or a physician of another specialty as the attending physician, relative to having a cardiologist, was associated with lower postdischarge use of beta-blockers, angiotensin-modifying agents and statins (ORs ranging from 0.46 to 0.82). Having an attending physician with 29 or more years experience, relative to having a physician who had graduated within the past 15 years, was associated with lower use of beta-blockers (OR 0.71, 95% CI 0.60-0.84) and statins (OR 0.81, 95% CI 0.67-0.97). INTERPRETATION: Patients who received care from noncardiologists and physicians with at least 29 years of experience had substantially lower use of evidence-based drug therapies after discharge. Dissemination strategies should be devised to improve the prescribing of evidence-based medications by these physicians.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".