Estimation of cancers caused by light to moderate alcohol consumption in the European Union
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it