Drinking pattern as a predictor of cardiovascular harm: A longitudinal study using alternative drinking pattern measures
Bibliographic record
Abstract
Background: This study compared measures of drinking pattern at baseline, and subsequent cardiovascular harm in a longitudinal study.Method: In Winnipeg, Manitoba, Canada, a community sample of 1154 adult men and women was interviewed at baseline in 1990 and 1991, then followed with all‐cause surveillance. Cox proportional hazards regressions were used to assess the “time to event” for morbidity or mortality from coronary heart disease (CHD), hypertension, or other cardiovascular disease. Surveillance was through a 10‐year series of documented physician visits, hospital discharges and deaths, classified by diagnosis. Drinking pattern was defined as either ⩾8 drinks (80–120 g of alcohol or more) at a sitting in the past 12 months, a report of feeling the effects, or ⩾5 usual drinks/day.Results: There were 104 individuals with CHD events in the data. When ⩾8 drinks at a sitting was the predictor, there were significant hazards for CHD among both men and women [hazard ratio (HR) = 2.32 and 1.07; p = 0.004 and 0.04], and marginally significant hazards for hypertension among men (HR = 1.40; p = 0.08). When feeling the effects or ⩾5 usual drinks/day were the predictors, there were no significant hazards of drinking pattern.Conclusion: Eight or more drinks was a stronger predictor of cardiovascular harm in these data than were feeling the effects or ⩾5 usual drinks.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".