Method for moderation: measuring lifetime risk of alcohol‐attributable mortality as a basis for drinking guidelines
Bibliographic record
Abstract
The objective of this paper was to determine separately the lifetime risk of drinking alcohol for chronic disease and acute injury outcomes as a basis for setting general population drinking guidelines for Australia. Relative risk data for different levels of average consumption of alcohol were combined with age, sex, and disease-specific risks of dying from an alcohol-attributable chronic disease. For injury, combinations of the number of drinks per occasion and frequency of drinking occasions were combined to model lifetime risk of death for different drinking pattern scenarios. A lifetime risk of injury death of 1 in 100 is reached for consumption levels of about three drinks daily per week for women, and three drinks five times a week for men. For chronic disease death, lifetime risk increases by about 10% with each 10-gram (one drink) increase in daily average alcohol consumption, although risks are higher for women than men, particularly at higher average consumption levels. Lifetime risks for injury and chronic disease combine to overall risk of alcohol-attributable mortality. In terms of guidelines, if a lifetime risk standard of 1 in 100 is set, then the implications of the analysis presented here are that both men and women should not exceed a volume of two drinks a day for chronic disease mortality, and for occasional drinking three or four drinks seem tolerable.
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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 | 0.080 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".