Smoking and Helicobacter pylori infection: an individual participant pooled analysis (Stomach Cancer Pooling- StoP Project)
Bibliographic record
Abstract
Smoking has been associated with acquisition and increased persistence of Helicobacter pylori infection, as well as with lower effectiveness of its eradication. A greater prevalence of infection among smokers could contribute to the increased risk for gastric cancer. We aimed to estimate the association between smoking and seropositivity to H. pylori through an individual participant data pooled analysis using controls from 14 case-control studies participating in the Stomach Cancer Pooling Project. Summary odds ratios and prevalence ratios (PRs), adjusted for age, sex and social class, and the corresponding 95% confidence intervals (CIs) were estimated through random-effects meta-analysis. Heterogeneity was quantified using the I statistic and publication bias with Egger's test. There was no significant association between smoking (ever vs. never) and H. pylori seropositivity (adjusted odds ratio = 1.08; 95% CI: 0.89-1.32; adjusted PR = 1.01; 95% CI: 0.98-1.05). The strength of the association did not increase with the intensity or duration of smoking; stratified analyses according to sex, age, region or type of sample did not yield a consistent pattern of variation or statistically significant results, except for participants younger than 55 years and who had been smoking for more than 30 years (adjusted PR = 1.08; 95% CI: 1.02-1.15). This is the first collaborative analysis providing pooled estimates for the association between smoking and H. pylori seropositivity, based on detailed and uniform information and adjusting for major covariates. The results do not support an association between smoking and H. pylori infection.
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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 | 0.045 | 0.068 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.050 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".