Abstract 3586: Alcohol Consumption and One-Year Angina Risk After Myocardial Infarction
Bibliographic record
Abstract
Background: Prior studies show light to moderate alcohol use is associated with reduced mortality and cardiovascular events, whereas heavy use increases mortality and cardiovascular risks. The association of alcohol use and post-myocardial infarction (MI) symptoms is unknown. We explored the association between alcohol use and risk of having angina 1 year after an MI. Methods: Upon enrollment in the 19-center prospective PREMIER registry, acute MI patients (n=2481) were asked about alcohol use. Angina (any vs. none) was assessed at 1 year with the Seattle Angina Questionnaire. The association of alcohol use and 1-year angina was modeled using a hierarchical multivariable modified Poisson regression model. Results: Overall, 47% reported never drinking and others reported having the following # of drinks/day: 42% < 1; 6% 1 to 2; 3% > 2 to 4; 2% > 4. After adjusting for demographic, clinical, and treatment variables, patients that reported never drinking were 45% more likely to have angina than moderate drinkers (1 to 2 drinks/day). However, > drinks/day was associated with an 81% greater risk of angina than moderate alcohol use. Those drinking < 1 drink/day or > 2 to 4 per day had similar angina risk compared to moderate drinkers. Results did not vary by gender (p > .05 for interaction). Conclusions: This study extends prior evidence of a dose-dependent relationship between alcohol use and other cardiovascular benefits/risks to post-MI angina. Moderate alcohol consumption (1 to 2 drinks/day) was associated with reduced risk of angina 1 year after MI compared to abstinence or heavy alcohol consumption. Excessive alcohol use (>4 drinks/day) was associated with increased risk of angina.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".