Alcohol consumption and gastric cancer risk—A pooled analysis within the StoP project consortium
Bibliographic record
Abstract
An association between heavy alcohol drinking and gastric cancer risk has been recently reported, but the issue is still open to discussion and quantification. We investigated the role of alcohol drinking on gastric cancer risk in the "Stomach cancer Pooling (StoP) Project," a consortium of epidemiological studies. A total of 9,669 cases and 25,336 controls from 20 studies from Europe, Asia and North America were included. We estimated summary odds-ratios (ORs) and the corresponding 95% confidence intervals (CIs) by pooling study-specific ORs using random-effects meta-regression models. Compared with abstainers, drinkers of up to 4 drinks/day of alcohol had no increase in gastric cancer risk, while the ORs were 1.26 (95% CI, 1.08-1.48) for heavy (>4 to 6 drinks/day) and 1.48 (95% CI 1.29-1.70) for very heavy (>6 drinks/day) drinkers. The risk for drinkers of >4 drinks/day was higher in never smokers (OR 1.87, 95% CI 1.35-2.58) as compared with current smokers (OR 1.14, 95% CI 0.93-1.40). Somewhat stronger associations emerged with heavy drinking in cardia (OR 1.61, 95% CI 1.11-2.34) than in non-cardia (OR 1.28, 95% CI 1.13-1.45) gastric cancers, and in intestinal-type (OR 1.54, 95% CI 1.20-1.97) than in diffuse-type (OR 1.29, 95% CI 1.05-1.58) cancers. The association was similar in strata of H. pylori infected (OR = 1.52, 95% CI 1.16-2.00) and noninfected subjects (OR = 1.69, 95% CI 0.95-3.01). Our collaborative pooled-analysis provides definite, more precise quantitative evidence than previously available of an association between heavy alcohol drinking and gastric cancer risk.
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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 | 0.033 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.038 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".