The association between dietary fiber intake and gastric cancer: a pooled analysis of 11 case–control studies
Bibliographic record
Abstract
PURPOSE: Gastric cancer (GC) is among the leading causes of cancer mortality worldwide. The objective of this study was to investigate the association between dietary fiber intake and GC. METHODS: We pooled data from 11 population or hospital-based case-control studies included in the Stomach Cancer Pooling (StoP) Project, for a total of 4865 histologically confirmed cases and 10,626 controls. Intake of dietary fibers and other dietary factors was collected using food frequency questionnaires. We calculated the odds ratios (OR) and 95% confidence intervals (CI) of the association between dietary fiber intake and GC by using a multivariable logistic regression model adjusted for study site, sex, age, caloric intake, smoking, fruit and vegetable intake, and socioeconomic status. We conducted stratified analyses by these factors, as well as GC anatomical site and histological type. RESULTS: The OR of GC for an increase of one quartile of fiber intake was 0.91 (95% CI: 0.85, 0.97), that for the highest compared to the lowest quartile of dietary fiber intake was 0.72 (95% CI: 0.59, 0.88). Results were similar irrespective of anatomical site and histological type. CONCLUSION: Our analysis supports the hypothesis that dietary fiber intake may exert a protective effect on GC.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".