Dietary intake of vitamin C and gastric cancer: a pooled analysis within the Stomach cancer Pooling (StoP) Project
Bibliographic record
Abstract
BACKGROUND: Previous studies suggest that dietary vitamin C is inversely associated with gastric cancer (GC), but most of them did not consider intake of fruit and vegetables. Thus, we aimed to evaluate this association within the Stomach cancer Pooling (StoP) Project, a consortium of epidemiological studies on GC. METHODS: Fourteen case-control studies were included in the analysis (5362 cases, 11,497 controls). We estimated odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for the association between dietary intake of vitamin C and GC, adjusted for relevant confounders and for intake of fruit and vegetables. The dose-response relationship was evaluated using mixed-effects logistic models with second-order fractional polynomials. RESULTS: Individuals in the highest quartile of dietary vitamin C intake had reduced odds of GC compared with those in the lowest quartile (OR: 0.64; 95% CI: 0.58, 0.72). Additional adjustment for fruit and vegetables intake led to an OR of 0.85 (95% CI: 0.73, 0.98). A significant inverse association was observed for noncardia GC, as well as for both intestinal and diffuse types of the disease. The results of the dose-response analysis showed decreasing ORs of GC up to 150-200 mg/day of vitamin C (OR: 0.54; 95% CI: 0.41, 0.71), whereas ORs for higher intakes were close to 1.0. CONCLUSIONS: The findings of our pooled study suggest that vitamin C is inversely associated with GC, with a potentially beneficial effect also for intakes above the currently recommended daily intake (90 mg for men and 75 mg for women).
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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.021 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".