Coffee consumption and gastric cancer: a pooled analysis from the Stomach cancer Pooling Project consortium
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate and quantify the relationship between coffee and gastric cancer using a uniquely large dataset from an international consortium of observational studies on gastric cancer, including data from 18 studies, for a total of 8198 cases and 21 419 controls. METHODS: A two-stage approach was used to obtain the pooled odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) for coffee drinkers versus never or rare drinkers. A one-stage logistic mixed-effects model with a random intercept for each study was used to estimate the dose-response relationship. Estimates were adjusted for sex, age and the main recognized risk factors for gastric cancer. RESULTS: Compared to never or rare coffee drinkers, the estimated pooled OR for coffee drinkers was 1.03 (95% CI, 0.94-1.13). When the amount of coffee intake was considered, the pooled ORs were 0.91 (95% CI, 0.81-1.03) for drinkers of 1-2 cups per day, 0.95 (95% CI, 0.82-1.10) for 3-4 cups, and 0.95 (95% CI, 0.79-1.15) for five or more cups. An OR of 1.20 (95% CI, 0.91-1.58) was found for heavy coffee drinkers (seven or more cups of caffeinated coffee per day). A positive association emerged for high coffee intake (five or more cups per day) for gastric cardia cancer only. CONCLUSIONS: These findings better quantify the previously available evidence of the absence of a relevant association between coffee consumption and gastric cancer.
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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 | 0.038 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".