Editorial: Brief Interventions for Risky Drinkers
Bibliographic record
Abstract
Alcohol consumption is a wholly or contributory cause for more than 200 diseases, injuries, and other health conditions with three-digit ICD-10 codes ( 1). Globally, alcohol is the fifth most important risk factor for ill-health and premature death (2). Risky alcohol use can be defined as a quantity or pattern of alcohol use that places individuals at risk for adverse health and social outcomes (3). Harmful use, in turn, can be defined as alcohol use that results in physical, psychological, or social harm (3). Using a threshold of an average of 60 g of alcohol/day for a man and 40 g/day for a woman (4), about one in four Europeans aged 15-64 years use alcohol in a risky fashion (5). And, using a threshold of an average of 100 g of alcohol/day for a man and 60 g/day for a woman, about one in eight of Europeans aged 15-64 years use alcohol in a harmful fashion (5). Harmful use causes comorbid illnesses such as liver disease, depression, and raised blood pressure (6). Risky and harmful alcohol use and their comorbid illnesses are frequently detected in primary health care, emergency departments, and other non-specialized clinical settings. Brief advice emerged in the 1980s (7-9) and progressed during the three following decades as a strategy to reduce risky and harmful alcohol use in non-specialized clinical settings (10). This article provides an update of the state-of-the art of brief advice.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.045 | 0.022 |
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".