Fruits and vegetables intake and gastric cancer risk: A pooled analysis within the Stomach cancer Pooling Project
Bibliographic record
Abstract
A low intake of fruits and vegetables is a risk factor for gastric cancer, although there is uncertainty regarding the magnitude of the associations. In our study, the relationship between fruits and vegetables intake and gastric cancer was assessed, complementing a previous work on the association betweenconsumption of citrus fruits and gastric cancer. Data from 25 studies (8456 cases and 21 133 controls) with information on fruits and/or vegetables intake were used. A two-stage approach based on random-effects models was used to pool study-specific adjusted (sex, age and the main known risk factors for gastric cancer) odds ratios (ORs) and the corresponding 95% confidence intervals (CIs). Exposure-response relations, including linear and nonlinear associations, were modeled using one- and two-order fractional polynomials. Gastric cancer risk was lower for a higher intake of fruits (OR: 0.76, 95% CI: 0.64-0.90), noncitrus fruits (OR: 0.86, 95% CI: 0.73-1.02), vegetables (OR: 0.68, 95% CI: 0.56-0.84), and fruits and vegetables (OR: 0.61, 95% CI: 0.49-0.75); results were consistent across sociodemographic and lifestyles categories, as well as study characteristics. Exposure-response analyses showed an increasingly protective effect of portions/day of fruits (OR: 0.64, 95% CI: 0.57-0.73 for six portions), noncitrus fruits (OR: 0.71, 95% CI: 0.61-0.83 for six portions) and vegetables (OR: 0.51, 95% CI: 0.43-0.60 for 10 portions). A protective effect of all fruits, noncitrus fruits and vegetables was confirmed, supporting further dietary recommendations to decrease the burden of gastric cancer.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".