Alcohol Use and Cancers of the Gastrointestinal Tract. Epidemiology and Preventive Implications
Bibliographic record
Abstract
Introduction: Alcohol is a carcinogen for human cancer. This contribution summarizes the relationships between alcohol use and gastrointestinal cancers, and implications for prevention. Methods: Comparative risk assessment and narrative literature review Results: The following gastrointestinal cancer sites were found to be causally impacted by alcohol use: lip and oral cavity, pharynx other than nasopharynx, esophagus, colon and rectum, and liver. Globally, 368,000 deaths (304,000 men and 64,000 women) and more than 10 million disability-adjusted life years (DALYs) lost (10.1 million; 8.4 million men and 1.6 million women) in 2016 were attributable to alcohol use, making up about 10% of all deaths and DALYs lost due to these cancers, respectively. There are effective and cost-effective alcohol control policies available to reduce this burden, namely the best buys of increasing taxation, reducing availability, and banning advertisement. In addition, public knowledge about the alcohol-cancer link should be increased. Discussion: There are a number of assumptions underlying these estimates, but overall all of them seem to be conservative.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".