The Effectiveness of β-Blockers After Myocardial Infarction in Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Beta-blocker therapy has been proven to reduce mortality and reinfarction after myocardial infarction (MI), but the impact of beta-blockers on cardiac outcomes in patients with type 2 diabetes in routine practice is not clear. The purpose of this study was to determine the effectiveness of beta-blockers after MI in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: Using the Saskatchewan Health Databases, 12,272 patients with newly treated diabetes were identified between 1991 and 1996; 625 patients were subsequently admitted for MI. Beta-blocker exposure within 30 days of discharge was identified in 298 patients, and all were followed until death, coverage termination, or 31 December 1999. Multivariate proportional hazards models were used to assess differences in all-cause mortality, recurrent MI, and 30-day all-cause rehospitalization (the latter a proxy measure for drug safety). RESULTS: Patients were aged 69 +/- 11 years old, 66% were male, and mean follow-up was 2.7 +/- 2.1 years. Overall, beta-blockers were prescribed for 48% of patients. There were fewer deaths in the beta-blocker group versus control subjects (55 of 298 [18.5%] vs. 126 of 327 [38.5%], respectively, P < 0.001). However, beta-blockers were not associated with improved survival in multivariate analyses (hazard ratio [HR] 0.89 [95% CI 0.63-1.25]). There were no differences in rates of recurrent MI (adjusted HR 1.35 [0.93-1.95]) or rehospitalizations (adjusted odds ratio 1.40 [0.83-2.37]) between the groups. CONCLUSIONS: Beta-blocker therapy post-MI was not associated with reduced mortality or fewer recurrent events in people with type 2 diabetes in routine practice, although these medications were safe in this population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".