Bibliographic record
Abstract
BACKGROUND: Cancers constitute a major non-communicable disease category globally and in the European Union (EU). SUMMARY: Alcohol use has been established as a major cause of cancer in humans. Principal cancer agencies agree that the following cancer sites are causally impacted by alcohol: lip and oral cavity, pharynx (excluding nasopharynx), oesophagus, colon and rectum, liver, (female) breast, and larynx. For all of these cancer sites, there is a dose-response relationship with no apparent threshold: the higher the average level of consumption, the higher the risk of cancer incidence. In the EU in 2016, about 80,000 people died of alcohol-attributable cancer, and about 1.9 million years of life were lost due to premature mortality or due to disability. Key messages: Given the above-described impact of alcohol on cancer, public awareness about the alcohol-cancer link needs to be increased. In addition, effective alcohol policy measures should be implemented. As a large part of alcohol-attributable cancers are in low and moderate alcohol users, in particular for females, general population measures such as increases in taxation, restrictions on availability, and bans on marketing and advertisement are best suited to reduce the alcohol-attributable cancer burden.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".