Estimation of cancers caused by light to moderate alcohol consumption in the European Union
Bibliographic record
Abstract
BACKGROUND: Research has identified alcohol to be an important risk factor for several types of cancers. This study estimates the number of incident cancers attributable to alcohol consumption in the European Union (EU) in 2017, with a special focus on those caused by light to moderate drinking levels. METHODS: The attributable-fraction methodology is used to estimate the number of new cancer cases in the year 2017 in the EU caused by alcohol use, and further examines those due to light to moderate drinking levels, defined here as alcohol consumption of <20 g of pure alcohol per day. RESULTS: Light to moderate drinking levels of alcohol caused almost 23 000 new cancer cases in the EU in 2017, and accounted for 13.3% of all alcohol-attributable cancers, and 2.3% of all cases of the seven alcohol-related cancer types. Almost half of these (∼11 000 cases) were female breast cancers. Also, more than a third of the cancer cases due to light to moderate drinking resulted from a light drinking level of <1 standard drink per day (total: 37%; women: 40%; men: 32%). CONCLUSIONS: Alcohol use, including light to moderate drinking, continues to cause considerable cancer burden, and efforts should be made to reduce this burden. In addition to the alcohol control policies suggested by the World Health Organization, public information campaigns and the placement of warning labels on alcohol containers advising of the cancer risk associated with alcohol use should be initiated to increase knowledge about the alcohol-cancer link.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".