Implications of Cardioprotective Assumptions for National Drinking Guidelines and Alcohol Harm Monitoring Systems
Bibliographic record
Abstract
The existence and potential level of cardioprotection from alcohol use is contested in alcohol studies. Assumptions regarding the risk relationship between alcohol use and ischaemic heart disease (IHD) are critical when providing advice for national drinking guidelines and for designing alcohol harm monitoring systems. We use three meta-analyses regarding alcohol use and IHD risk to investigate how varying assumptions lead to differential estimates of alcohol-attributable (AA) deaths and weighted relative risk (RR) functions, in Australia and Canada. Alcohol exposure and mortality data were acquired from administrative sources and AA fractions were calculated using the International Model of Alcohol Harms and Policies. We then customized a recent Global Burden of Disease (GBD) analysis to inform drinking guidelines internationally. Australians drink slightly more than Canadians, per person, but are also more likely to identify as lifetime abstainers. Cardioprotective scenarios resulted in substantial differences in estimates of net AA deaths in Australia (between 2933 and 4570) and Canada (between 5179 and 8024), using GBD risk functions for all other alcohol-related conditions. Country-specific weighted RR functions were analyzed to provide advice toward drinking guidelines: Minimum risk was achieved at or below alcohol use levels of 10 g/day ethanol, depending on scenario. Consumption levels resulting in 'no added' risk from drinking were found to be between 10 and 15 g/day, by country, gender, and scenario. These recommendations are lower than current guidelines in Australia, Canada, and some other high-income countries: These guidelines may be in need of downward revision.
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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 | 0.324 | 0.625 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".