Index-based dietary patterns in relation to gastric cancer risk: a systematic review and meta-analysis
Bibliographic record
Abstract
Dietary indices are widely used in diet quality measurement, and the index-based dietary patterns are related to gastric cancer risk. To evaluate the relationship between different kinds of index-based dietary patterns and gastric cancer risk, we systematically searched four English-language databases and four Chinese-language databases. The quality of studies was assessed by the Newcastle–Ottawa Scale. Meta-analyses were performed to estimate the association between gastric cancer incidence and different types of index-based dietary patterns. The OR and hazard ratios (HR) of gastric cancer incidence were calculated by regression models in case–control studies and prospective cohort studies, respectively. The studies were pooled in the random effects model to calculate the summarised risk estimate of the highest quantile interval of dietary indices, taking the lowest as the referent. The dietary indices included different versions of Mediterranean diet score (MDS) and dietary inflammatory index (DII), healthy eating index, Chinese Food Pagoda score and food index score. The meta-analysis was carried out for studies on MDS and DII. The combined OR of gastric cancer for the highest MDS v. the referent was 0·42 (95 % CI 0·2, 0·86), and the combined HR was 0·89 (95 % CI 0·68, 1·17). The combined OR for DII was 2·11 (95 % CI 1·41, 3·15). Higher Mediterranean dietary pattern consumption might reduce gastric cancer risk, while higher inflammatory diet pattern consumption might increase gastric cancer risk.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".