Antibiotics Associated With Increased Risk of New-Onset Crohn’s Disease But Not Ulcerative Colitis: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to perform a meta-analysis investigating antibiotic exposure as a risk factor for developing inflammatory bowel disease (IBD). METHODS: A literature search using Medline, Embase, and Cochrane databases was performed to identify studies providing data on the association between antibiotic use and newly diagnosed IBD. Included studies reported Crohn's disease (CD), ulcerative colitis (UC), or a composite of both (IBD) as the primary outcome and evaluated antibiotic exposure before being diagnosed with IBD. A random-effects meta-analysis was conducted to determine overall pooled estimates and 95% confidence intervals (CIs). RESULTS: A total of 11 observational studies (8 case-control and 3 cohort) including 7,208 patients diagnosed with IBD were analyzed. The pooled odds ratio (OR) for IBD among patients exposed to any antibiotic was 1.57 (95% CI 1.27-1.94). Antibiotic exposure was significantly associated with CD (OR 1.74, 95% CI 1.35-2.23) but was not significant for UC (OR 1.08, 95% CI 0.91-1.27). Exposure to antibiotics most markedly increased the risk of CD in children (OR 2.75, 95% CI 1.72-4.38). All antibiotics were associated with IBD, with the exception of penicillin. Exposure to metronidazole (OR 5.01, 95% CI 1.65-15.25) or fluoroquinolones (OR 1.79, 95% CI 1.03-3.12) was most strongly associated with new-onset IBD. CONCLUSIONS: Exposure to antibiotics appears to increase the odds of being newly diagnosed with CD but not UC. This risk is most marked in children diagnosed with 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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.074 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".