The prevalence of Helicobacter pylori infection in inflammatory bowel disease in China: A case-control study
Bibliographic record
Abstract
BACKGROUND & AIMS: Helicobacter pylori (H. pylori) infection remains high in China though the incidence of inflammatory bowel disease (IBD) has increased. Our aim was to investigate the relationship between the prevalence of H. pylori and inflammatory bowel disease. METHODS: Hospitalized IBD patients including Crohn's disease (CD) and ulcerative colitis (UC) who had tested H. pylori antibody were enrolled. Controls were chose from age- and sex- matched healthy physical examination people who had H. pylori antibody test in a 1:2 fashion (IBD patients:controls). IBD medical history was recorded. All patients were typed by the Montreal classification. Mayo Clinic score and the Harvey-Bradshaw Severity Index were used to evaluate their disease activity. Patients and controls that had H. pylori eradication therapy before were excluded. RESULTS: Two hundred and sixty IBD patients including 213 CD patients and 47 UC patients, and 520 controls were involved in this study. The prevalence of H. pylori infection in IBD patients (9.6%, 25/260) and IBD newly diagnosed patients (12.1%, 8/66), as well as CD patients (8.9%, 19/213) including CD newly diagnosed patients (10.6%, 5/47) and UC patients (12.8%, 6/47) was significantly lower than controls (29.8%, 155/520) (p = 2.796*10-10, 0.007, 5.723*10-9, 0.016, 0.014), while there was no statistically difference between UC newly diagnosed patients and the controls, and IBD patients with different disease type, disease activity and treatment history. CONCLUSIONS: H. pylori infection had a negative association with IBD, especially CD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".