Risk of common infections in people with inflammatory bowel disease in primary care: a population-based cohort study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the risk of common infections in individuals with inflammatory bowel disease (IBD) [ulcerative colitis and Crohn's disease] compared with matched controls in a contemporary UK primary care population. DESIGN: Matched cohort analysis (2014-2019) using the Royal College of General Practitioners Research and Surveillance Centre primary care database. Risk of common infections, viral infections and gastrointestinal infections (including a subset of culture-confirmed infections), and predictors of common infections, were evaluated using multivariable Cox proportional hazards models. RESULTS: 18 829 people with IBD were matched to 73 316 controls. People with IBD were more likely to present to primary care with a common infection over the study period (46% vs 37% of controls). Risks of common infections, viral infections and gastrointestinal infections (including stool culture-confirmed infections) were increased for people with ulcerative colitis and Crohn's disease compared with matched controls (HR range 1.12-1.83, all p<0.001). Treatment with oral glucocorticoid therapy, immunotherapies and biologic therapy, but not with aminosalicylates, was associated with increased infection risk in people with IBD. Despite mild lymphopenia and neutropenia being more common in people with IBD (18.4% and 1.9%, respectively) than in controls (6.5% and 1.5%, respectively), these factors were not associated with significantly increased infection risk in people with IBD. CONCLUSION: People with IBD are more likely to present with a wide range of common infections. Health professionals and people with IBD should remain vigilant for infections, particularly when using systemic corticosteroids, immunotherapies or biologic agents. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov (NCT03835780).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".