Meat intake and risk of gastric cancer in the Stomach cancer Pooling (StoP) project
Bibliographic record
Abstract
The consumption of processed meat has been associated with noncardia gastric cancer, but evidence regarding a possible role of red meat is more limited. Our study aims to quantify the association between meat consumption, namely white, red and processed meat, and the risk of gastric cancer, through individual participant data meta-analysis of studies participating in the "Stomach cancer Pooling (StoP) Project". Data from 22 studies, including 11,443 cases and 28,029 controls, were used. Study-specific odds ratios (ORs) were pooled through a two-stage approach based on random-effects models. An exposure-response relationship was modeled, using one and two-order fractional polynomials, to evaluate the possible nonlinear association between meat intake and gastric cancer. An increased risk of gastric cancer was observed for the consumption of all types of meat (highest vs. lowest tertile), which was statistically significant for red (OR: 1.24; 95% CI: 1.00-1.53), processed (OR: 1.23; 95% CI: 1.06-1.43) and total meat (OR: 1.30; 95% CI: 1.09-1.55). Exposure-response analyses showed an increasing risk of gastric cancer with increasing consumption of both processed and red meat, with the highest OR being observed for an intake of 150 g/day of red meat (OR: 1.85; 95% CI: 1.56-2.20). This work provides robust evidence on the relation between the consumption of different types of meat and gastric cancer. Adherence to dietary recommendations to reduce meat consumption may contribute to a reduction in the burden of 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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".