Irregular Heavy Drinking Occasions and Risk of Ischemic Heart Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Contrary to a cardioprotective effect of moderate regular alcohol consumption, accumulating evidence points to a detrimental effect of irregular heavy drinking occasions (>60 g of pure alcohol or > or =5 drinks per occasion at least monthly) on ischemic heart disease risk, even for drinkers whose average consumption is moderate. The authors systematically searched electronic databases from 1980 to 2009 for case-control or cohort studies examining the association of irregular heavy drinking occasions with ischemic heart disease risk. Studies were included if they reported either a relative risk estimate for intoxication or frequency of > or =5 drinks stratified by or adjusted for total average alcohol consumption. The search identified 14 studies (including 31 risk estimates) containing 4,718 ischemic heart disease events (morbidity and mortality). Using a standardized protocol, the authors extracted relative risk estimates and their variance, in addition to study characteristics. In a random-effects model, the pooled relative risk of irregular heavy drinking occasions compared with regular moderate drinking was 1.45 (95% confidence interval: 1.24, 1.70), with significant between-study heterogeneity (I(2) = 53.9%). Results were robust in several sensitivity analyses. The authors concluded that the cardioprotective effect of moderate alcohol consumption disappears when, on average, light to moderate drinking is mixed with irregular heavy drinking occasions.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".