Education and gastric cancer risk—An individual participant data meta‐analysis in the StoP project consortium
Bibliographic record
Abstract
Low socioeconomic position (SEP) is a strong risk factor for incidence and premature mortality from several cancers. Our study aimed at quantifying the association between SEP and gastric cancer (GC) risk through an individual participant data meta-analysis within the "Stomach cancer Pooling (StoP) Project". Educational level and household income were used as proxies for the SEP. We estimated pooled odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) across levels of education and household income by pooling study-specific ORs through random-effects meta-analytic models. The relative index of inequality (RII) was also computed. A total of 9,773 GC cases and 24,373 controls from 25 studies from Europe, Asia and America were included. The pooled OR for the highest compared to the lowest level of education was 0.60 (95% CI, 0.44-0.84), while the pooled RII was 0.45 (95% CI, 0.29-0.69). A strong inverse association was observed both for noncardia (OR 0.39, 95% CI, 0.22-0.70) and cardia GC (OR 0.47, 95% CI, 0.22-0.99). The relation was stronger among H. pylori negative subjects (RII 0.14, 95% CI, 0.04-0.48) as compared to H. pylori positive ones (RII 0.29, 95% CI, 0.10-0.84), in the absence of a significant interaction (p = 0.28). The highest household income category showed a pooled OR of 0.65 (95% CI, 0.48-0.89), while the corresponding RII was 0.40 (95% CI, 0.22-0.72). Our collaborative pooled-analysis showed a strong inverse relationship between SEP indicators and GC risk. Our data call for public health interventions to reduce GC risk among the more vulnerable groups of the population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".