New perspectives on how to formulate alcohol drinking guidelines
Bibliographic record
Abstract
BACKGROUND: Low-Risk Alcohol Drinking Guidelines (LRDGs) aim to reduce the harms caused by alcohol. However, considerable discrepancies exist in the 'low-risk' thresholds employed by different countries. ARGUMENT/ANALYSIS: Drawing upon Canada's LRDGs update process, the current paper offers the following propositions for debate regarding the establishment of 'low-risk' thresholds in national guidelines: (1) as an indicator of health loss, years of life lost (YLL) has several advantages that could make it more suitable for setting guidelines than deaths, premature deaths or disability adjusted years of life (DALYs) lost. (2) Presenting age-specific guidelines may not be the most appropriate way of providing LRDGs. (3) Given past overemphasis on the so-called protective effects of alcohol on health, presenting cause-specific guidelines may not be appropriate compared with a 'whole health' effect derived from a weighted composite risk function comprising conditions that are causally related to alcohol consumption. (4) To help people reduce their alcohol use, presenting different risk zones associated with alcohol consumption instead of a single low risk threshold may be advantageous. CONCLUSIONS: National LRDGs should be based on years of life lost and should be neither age-specific nor cause-specific. We recommend using risk zones rather than a single drinking threshold to help people assess their own risk and encourage the adoption of behaviours with positive health impacts across the alcohol use spectrum.
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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 | 0.041 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".