Citrus fruit intake and gastric cancer: The stomach cancer pooling (StoP) project consortium
Bibliographic record
Abstract
Diets rich in vegetables and fruit have been associated with reduced risk of gastric cancer, and there is suggestive evidence that citrus fruits have a protective role. Our study aimed at evaluating and quantifying the association between citrus fruit intake and gastric cancer risk. We conducted a one-stage pooled analysis including 6,340 cases and 14,490 controls from 15 case-control studies from the stomach cancer pooling (StoP) project consortium. Odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) of gastric cancer across study-specific tertiles of citrus fruit intake (grams/week) were estimated by generalized linear mixed effect models, with logistic link function and random intercept for each study. The models were adjusted for sex, age, and the main recognized risk factors for gastric cancer. Compared to the first third of the distribution, the adjusted pooled OR (95% CI) for the highest third was 0.80 (0.73-0.87). The favourable effect of citrus fruits increased progressively until three servings/week and leveled off thereafter. The magnitude of the association was similar between cancer sub-sites and histotypes. The analysis by geographic area showed no association in studies from the Americas. Our data confirm an inverse association between citrus fruits and gastric cancer and provide precise estimates of the magnitude of the association. However, the null association found in studies from America and in some previous cohort studies prevent to draw definite conclusions on a protective effect of citrus fruit consumption.
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.
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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.005 |
| 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".