Alcohol drinking cessation and its effect on esophageal and head and neck cancers: A pooled analysis
Bibliographic record
Abstract
The objective of this study was to conduct a pooled analysis to evaluate the strength of the evidence available in the epidemiological literature on the association between alcohol drinking cessation and reduction in esophageal and head and neck cancer risks. A search using several electronic bibliographic databases was performed for relevant epidemiological literature between 1966 and 2006. A total of 13 unique studies including over 5,000 cases were found. Categorical and third order polynomial (cubic) regression models were fitted to estimate the temporal relationship between years of drinking cessation and risk of cancer. The risk of esophageal cancer significantly increased within the first 2 yr following cessation [odds ratios (ORs)(0-2 yr): 2.50, 95% confidence intervals (CI): 2.23-2.80], then decreased rapidly and significantly after longer periods of abstention (OR(15+ yr): 0.37, 95% CI: 0.33-0.41). An elevated risk, although not strong as for esophageal cancer, was observed for head and neck cancer up to 10 yr of quitting drinking (OR(5-10 yr): 1.26, 95% CI: 1.18-1.35). Such risk only reduced after 10 yr of cessation (OR(10-16 yr): 0.67, 95% CI: 0.63-0.73). After more than 20 yr of alcohol cessation, the risks for both cancers were no longer significantly different from the risk of never drinkers. Our findings demonstrate an important role of alcohol cessation on esophageal and head and neck carcinogenesis.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.040 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".