Association between Alcohol Consumption and Pancreatic Cancer Risk: A Case-Control Study
Bibliographic record
Abstract
PURPOSE: Evidence is inconsistent regarding alcohol and pancreatic cancer risk, although heavy drinking may increase risk. METHODS: A population-based case-control study was conducted using 345 pancreas cancer cases diagnosed 2011-2012 and 1,285 frequency-matched controls from Ontario, Canada. Logistic regression was used to evaluate alcohol consumption and pancreatic cancer risk; data was also stratified by sex and smoking status to assess interaction. RESULTS: Alcohol consumption was not associated with pancreatic cancer risk (age-adjusted odds ratio=0.78, 95% CI: 0.58, 1.05 for 1 - 3 drinks/week; age-adjusted odds ratio=0.86, 95% CI: 0.63, 1.17 for 4 - 20 drinks/week), however there was a non-significant increased risk for heavy drinkers consuming ≥ 21 drinks/week (age-adjusted odds ratio=1.35, 95% CI: 0.81, 2.27). Cigarette smoking modified the alcohol-cancer relationship; among current smokers, heavy alcohol consumption was associated with a significantly increased pancreatic cancer risk (age-adjusted odds ratio=4.04, 95% CI: 1.58, 10.37), whereas this significant association with heavy drinking was not observed among non-smokers (age-adjusted odds ratio=2.01, 95% CI: 0.50, 8.18). Furthermore, light - moderate alcohol intake was associated with increased pancreas cancer risk among current smokers. CONCLUSIONS: While alcohol was not significantly associated with pancreatic cancer risk, smoking status modified this relationship such that among current smokers, alcohol intake was associated with a greater than two-fold increased risk of pancreatic cancer. The results should be interpreted with caution due to small sample sizes within subgroups and correction for multiple comparisons should be considered. These findings should be replicated in larger studies where more precise estimates of risk can be obtained.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".