Alcohol consumption and gastric cancer risk—A pooled analysis within the StoP project consortium
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it