Improving the epidemiology of low‐risk drinking guidelines is not enough
Bibliographic record
Abstract
Improving the epidemiology of low-risk drinking guidelines is not enough Work to improve the precision of the epidemiology underlying national low-risk drinking guidelines is important, but until the field engages more deeply in understanding how risk is interpreted, communicated and understood, guidelines will continue to have uncertain impacts.Shield et al. [1] draw upon the recent redevelopment of the Canadian Low Risk Drinking Guidelines to formulate some key principles that, they argue, should underpin future guidelines work internationally.This is an admirable attempt to further earlier work by Holmes et al. [2] arguing for increasing rigour and transparency in the guidelines setting process and offers much food for thought.Fundamentally, the setting of guidelines is concerned with risk,
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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 | 0.156 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".