Cancer incidence and mortality attributable to alcohol consumption
Bibliographic record
Abstract
Alcohol consumption is a major cause of disease and death. In a previous study, we reported that in 2002, 3.6% of all cases of cancer and a similar proportion of cancer deaths were attributable to the consumption of alcohol. We aimed to update these figures to 2012 using global estimates of cancer cases and cancer deaths, data on the prevalence of drinkers from the World Health Organization (WHO) global survey on alcohol and health, and relative risks for alcohol-related neoplasms from a recent meta-analysis. Over the 10-year period considered, the total number of alcohol-attributable cancer cases increased to approximately 770,000 worldwide (5.5% of the total number of cancer cases)-540,000 men (7.2%) and 230,000 women (3.5%). Corresponding figures for cancer deaths attributable to alcohol consumption increased to approximately 480,000 (5.8% of the total number of cancer deaths) in both sexes combined-360,000 (7.8%) men and 120,000 (3.3%) women. These proportions were particularly high in the WHO Western Pacific region, the WHO European region and the WHO South-East Asia region. A high burden of cancer mortality and morbidity is attributable to alcohol, and public health measures should be adopted in order to limit excessive alcohol consumption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".