Alcohol-Caused Mortality in Australia and Canada: Scenario Analyses Using Different Assumptions About Cardiac Benefit
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine how definitions of "abstainers" in prospective studies of alcohol and mortality influence estimates of the extent of coronary heart disease (CHD) protection due to low-risk alcohol consumption. METHOD: Meta-analyses were conducted on 35 prospective studies categorized according to the presence of up to two postulated errors for men and women regarding the classification of abstainers. Risk of death from CHD was estimated to calculate age-gender specific etiological fractions for application to mortality data for Australia and Canada in 2002. Controls for study characteristics were age, history of illness, and smoking status. Abstainers were classified as (1) lifelong abstainers, exdrinkers, and occasional drinkers--both classification errors present; (2) lifelong abstainers and exdrinkers--one error; and (3) lifelong abstainers only. "Low-risk consumption" was defined as up to 24 g, on average, per day for women and 44 g for men; "elevated risk consumption" was defined as more than 24 g on an average day for women and more than 44 g for men. Higher daily alcohol consumption was classified as "high risk." RESULTS: Significant CHD protection was found for both men (odds ratio [OR] = .79) and women (OR = .89) only in studies committing both errors; it was found for women only in studies with "occasional drinker" error (OR = .75) and for neither gender in the few available error-free studies. Estimates of net alcohol-caused deaths in 2002 varied accordingly, from -1,405 to 2,479 for Australia and from 4,321 to 7,319 for Canada. CONCLUSIONS: There is a need for CHD mortality studies that use lifelong abstinence as the reference point for estimating CHD protection. There may be gender differences in CHD protection. Separate estimates for the effects of low- and elevated-risk alcohol consumption on mortality should be made and communicated.
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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 | 0.065 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".