Use of time‐dependent measures to estimate benefits of β‐blockers after myocardial infarction
Bibliographic record
Abstract
PURPOSE: To estimate the reduction in all-cause mortality conferred by beta-blockers in a population-based cohort of elderly survivors of myocardial infarction during the year following hospital discharge. METHODS: A dynamic retrospective cohort was assembled from persons aged 66 years and over surviving myocardial infarction in Quebec between 1990 and 1993. Information on hospitalizations was linked to medication and physician claims, demographic characteristics and vital status. Subjects prescribed beta-blockers at hospital discharge had fewer comorbid medical conditions, less pre-existing cardiovascular disease and less severe infarcts. To control for these differences, analyzes were restricted to subjects receiving at least one beta-blocker and mortality was compared between periods with and without beta-blocker exposure using Cox proportional hazard models. RESULTS: Among 14,547 survivors of myocardial infarction, 41% were dispensed at least one beta-blocker. Among those subjects, the risk of dying during periods of beta-blocker use was reduced 40% (hazard ratio = 0.6; 95% CI: 0.5, 0.7). CONCLUSION: Confounding by indication threatens the validity of observational studies of intended effects of medications. For elderly survivors of myocardial infarction, the estimated benefit of beta-blockers from observational studies is greater than the estimate from randomized trials. Greater benefits do not seem to be an artifact arising from systematically prescribing beta-blockers to subjects with better prognosis. Reducing confounding by indication can enhance the validity of observational studies of medications and widen research applications of administrative health databases. While the actual benefits of medications are never truly known these studies can provide a credible range that brackets the truth.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".