Binge Drinking and Mortality After Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Moderate drinkers have a lower risk of mortality after myocardial infarction (MI). Although binge drinking has been associated with a higher risk of MI in some studies, its relation to prognosis after MI is uncertain. METHODS AND RESULTS: In a prospective, inception cohort study conducted at 45 US hospitals, 1935 patients hospitalized with a confirmed MI between 1989 and 1994 underwent detailed personal interviews. Patients reported their usual frequency of binge drinking of beer, wine, and liquor, defined as intake of 3 or more drinks within 1 to 2 hours, and were followed up for mortality for a median of 3.8 years. Of 1919 eligible patients, 250 (94% men) reported binge drinking during the prior year, and a total of 318 patients died during follow-up. Binge drinkers had a 2-fold higher risk of mortality than drinkers who did not binge (hazard ratio, 2.0; 95% confidence interval, 1.3 to 3.0). A comparison of 192 binge drinkers and 192 other patients matched on propensity scores yielded a similar result. The association between binge drinking and total mortality tended to be similar among patients whose usual alcohol intake was light or heavier and for binge drinkers who consumed beer, wine, or liquor. Usual alcohol intake was inversely associated with mortality, but binge drinking completely attenuated this relation. CONCLUSIONS: Our results suggest that alcohol consumption may be linked to potential hazards among patients who survive acute MI. Although moderate intake has been associated with lower mortality, binge drinking, even among light drinkers, appears to be associated with 2-fold higher mortality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".