Alcohol Volume, Drinking Pattern, and Cardiovascular Disease Morbidity and Mortality: Is There a U-shaped Function?
Bibliographic record
Abstract
The health effects of a binge pattern of alcohol consumption have not been widely investigated. The objective of this study was to evaluate the cardiovascular consequences of binge drinking (consumption of eight or more drinks at one sitting) and usual (nonbinge) drinking in a longitudinal, population-based study. Data obtained from 1,154 men and women aged 18-64 years interviewed in Winnipeg, Manitoba, Canada, in 1990 and 1991 were linked to health care utilization and mortality records. Using an 8-year follow-up period, the authors performed separate Cox proportional hazards regression analyses for men and women on time to first event for physician visits, hospitalizations, and deaths due to coronary heart disease, hypertension, and other cardiovascular disease. Binge drinking increased the risk of coronary heart disease in both men (hazard ratio (HR) = 2.26, 95% confidence interval (CI): 1.22, 4.20) and women (HR = 1.10, 95% CI: 1.02, 1.18). It increased the risk of hypertension in men (HR = 1.57, 95% CI: 1.04, 2.35) but not in women. Binge drinking had no effect on the risk of other cardiovascular disease. In contrast, usual drinking had significant cardioprotective effects in both men and women. Thus, the harmful effects of binge drinking on cardiovascular disease morbidity and mortality can be disaggregated from the protective effects of usual drinking at various levels of consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".