Alcohol Intake and Pancreatic Cancer Risk: A Pooled Analysis of Fourteen Cohort Studies
Bibliographic record
Abstract
BACKGROUND: Few risk factors have been implicated in pancreatic cancer etiology. Alcohol has been theorized to promote carcinogenesis. However, epidemiologic studies have reported inconsistent results relating alcohol intake to pancreatic cancer risk. METHODS: We conducted a pooled analysis of the primary data from 14 prospective cohort studies. The study sample consisted of 862,664 individuals among whom 2,187 incident pancreatic cancer cases were identified. Study-specific relative risks and 95% confidence intervals were calculated using Cox proportional hazards models and then pooled using a random effects model. RESULTS: A slight positive association with pancreatic cancer risk was observed for alcohol intake (pooled multivariate relative risk, 1.22; 95% confidence interval, 1.03-1.45 comparing >or=30 to 0 grams/day of alcohol; P value, test for between-studies heterogeneity=0.80). For this comparison, the positive association was only statistically significant among women although the difference in the results by gender was not statistically significant (P value, test for interaction=0.19). Slightly stronger results for alcohol intake were observed when we limited the analysis to cases with adenocarcinomas of the pancreas. No statistically significant associations were observed for alcohol from wine, beer, and spirits comparing intakes of >or=5 to 0 grams/day. A stronger positive association between alcohol consumption and pancreatic cancer risk was observed among normal weight individuals compared with overweight and obese individuals (P value, test for interaction=0.01). DISCUSSION: Our findings are consistent with a modest increase in risk of pancreatic cancer with consumption of 30 or more grams of alcohol per day.
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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 | 0.037 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".