Risk of Myocardial Infarction Immediately After Alcohol Consumption
Bibliographic record
Abstract
BACKGROUND: Habitual moderate alcohol consumption is associated with a lower risk of acute myocardial infarction (MI), whereas heavy (binge) drinking is associated with higher cardiovascular risk. However, less is known about the immediate effects of alcohol consumption on the risk of acute MI and whether any association differs by beverage type or usual drinking patterns. METHODS: We conducted a case-crossover analysis of 3869 participants from the Determinants of Myocardial Infarction Onset Study who were interviewed during hospitalization for acute MI in one of the 64 medical centers across the United States in 1989-1996. We compared the observed number of times that each participant consumed wine, beer, or liquor in the hour preceding MI symptom onset with the expected frequency based on each participant's control information, defined as the number of times the participant consumed alcohol in the past year. RESULTS: Among 3869 participants, 2119 (55%) reported alcohol consumption in the past year, including 76 within 1 hour before acute MI onset. The incidence rate of acute MI onset was elevated 1.72-fold (95% confidence interval [CI] = 1.37-2.16) within 1 hour after alcohol consumption. The association was stronger for liquor than for beer or wine. The higher rate was not apparent for daily drinkers. For the 24 hours after consumption, there was a 14% lower rate (relative risk = 0.86 [95% CI = 0.79-0.95]) of MI compared with periods with no alcohol consumption. CONCLUSIONS: Alcohol consumption is associated with an acutely higher risk of MI in the subsequent hour among people who do not typically drink alcohol daily.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".