Systematic review with meta‐analysis: association between <i>Helicobacter pylori</i> CagA seropositivity and odds of inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Accumulating data support a protective role of Helicobacter pylori against inflammatory bowel diseases (IBD), which might be mediated by strain-specific constituents, specifically cagA expression. AIM: To perform a systematic review and meta-analysis to more clearly define the association between CagA seropositivity and IBD. METHODS: We identified comparative studies that included sufficient detail to determine the odds or risk of IBD, Crohn's disease (CD) or ulcerative colitis (UC) amongst individuals with vs without evidence of cagA expression (eg CagA seropositivity). Estimates were pooled using a random effects model. RESULTS: Three clinical studies met inclusion criteria. cagA expression was represented by CagA seropositivity in all studies. Compared to CagA seronegativity overall, CagA seropositivity was associated with lower odds of IBD (OR 0.31, 95% CI 0.21-0.44) and CD (OR 0.25, 95% CI 0.17-0.38), and statistically nonsignificant lower odds for UC (OR 0.68, 95% CI 0.35-1.32). Similarly, compared to H pylori non-exposed individuals, H pylori exposed, CagA seropositive individuals had lower odds of IBD (OR 0.26, 95% CI 0.16-0.41) and CD (OR 0.23, 95% CI 0.15-0.35), but not UC (OR 0.66, 0.34-1.27). However, there was no significant difference in the odds of IBD, CD or UC between H pylori exposed, CagA seronegative and H pylori non-exposed individuals. CONCLUSION: We found evidence for a significant association between CagA seropositive H pylori exposure and reduced odds of IBD, particularly CD, but not for CagA seronegative H pylori exposure. Additional studies are needed to confirm these findings and define underlying mechanisms.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".