Dietary intake of copper and gastric cancer: a pooled analysis within the Stomach cancer Pooling (StoP) Project
Bibliographic record
Abstract
BACKGROUND: Evidence on the potential association between dietary copper intake and gastric cancer (GC) is lacking. Thus, we aimed to evaluate this association within the Stomach cancer Pooling (StoP) Project-an international consortium of epidemiological studies on GC. METHODS: Data from five case-control studies within the StoP Project were included (2448 cases, 4350 controls). We estimated adjusted odds ratios (ORs) and 95% CIs for the association between dietary copper intake and GC using multivariable mixed-effects logistic regression models. We also modelled the dose-response relationship between copper intake and GC using a logistic mixed-effects model with fractional polynomial. RESULTS: The OR for the highest quartile of copper intake compared with the lowest one was 0.78 (95% CI: 0.63-0.95; P for trend = 0.013). Results were similar for non-cardia-type (OR: 0.72; 95% CI: 0.57-0.91), intestinal-type (OR: 0.75; 95% CI: 0.56-0.99) and other histological-type GC (OR: 0.65; 95% CI: 0.44-0.96). The dose-response analysis showed a steep decrease in ORs for modest intakes (<1 mg/day), which were subsequently steady for ≤3 mg/day (OR: 0.09; 95% CI: 0.02-0.41) and slowly increased for higher intakes. CONCLUSIONS: The findings of our large study suggest that copper intake might be inversely associated with GC, although their confirmation by prospective studies is required.
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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 | 0.037 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".