Association Between Helicobacter pylori Infection and Ulcerative Colitis: A Meta-Analysis Study
Bibliographic record
Abstract
Background: Ulcerative colitis (UC), a chronic inflammatory disease that can cause bloody diarrhea, remains a major global disease burden. While Helicobacter pylori infection is postulated to be able to reduce the occurrence of UC, its role in the disease itself remains contentious. Hence, this meta-analysis aims to examine whether H. pylori infection can lower the chance of developing UC.Method: A systematic search was conducted through three electronic databases, namely Cochrane, PubMed, and Embase, with the addition of individual hand searching to analyze the association between ulcerative colitis and H. pylori infection in the adult population. Relevant articles selected through eligibility criteria were assessed for quality by using the Newcastle-Ottawa Scale. Furthermore, a random-effects meta-analysis was conducted to estimate the pooled odd ratios (ORs) along with their 95% confidence intervals (CIs). Higgins test and funnel plots were also conducted.Results: A total of 11,498 patients with UC and 356,130 controls from 22 studies were included in the meta-analysis. Included studies showed fair or good quality. Good quality was achieved with the minimum score of 3 stars for selection, 1 star for comparability, and 2 stars for outcome/exposure, while fair quality was achieved with the minimum score of 2 stars for domain, 1 star for comparability, and 2 stars for outcome/exposure. Our findings indicated that H. pylori infection was associated with lower odds of UC [pooled ORs 0.51 (95% CI: 0.46-0.56)]), albeit moderate heterogeneity (I2= 54%, p = 0.002). Furthermore, publication bias was not found.Conclusion: The present study adds to the growing body of evidence supporting the potential protective effects of H. pylori infection on the occurrence of UC. However, further primary research with prospective study design needs to be conducted to confirm our findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.054 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".