Labels warning about alcohol-attributable cancer risks should be mandated urgently
Bibliographic record
Abstract
In January, 2025, the US Surgeon General released an Advisory on alcohol consumption and cancer risk.1 He briefly summarised the evidence on alcohol and cancer, including underlying biological mechanisms, and concluded that alcohol use is a leading preventable cause of cancer in the USA and globally, causing around 100 000 and 750 000 cancer cases annually, respectively.2 The carcinogenicity of alcohol is not a new concept. More than three decades ago, the International Agency for Research on Cancer (IARC) and the Continuous Update Project of the World Cancer Research Fund/American Institute for Cancer Research concluded that there was sufficient evidence that alcohol causes certain cancers;3 the current list of alcohol-attributable cancer sites published by IARC4 includes cancers of the oral cavity, oropharynx, hypopharynx, oesophagus (squamous cell carcinoma), colon, rectum, liver, and intra-hepatic bile duct, larynx, and female breast.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.034 | 0.025 |
| Insufficient payload (model declined to judge) | 0.029 | 0.017 |
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".