Clostridioides difficile Infection in Children With Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: The study's objective was to investigate the incidence and risk factors associated with Clostridioides difficile (previously known as Clostridium) infection (CDI) in children with inflammatory bowel disease (IBD) in the province of Manitoba. METHODS: Our longitudinal population-based cohort was comprised of all children and young adults aged <17 years diagnosed with IBD in the Canadian province of Manitoba between 2011 and 2019. The diagnosis of CDI was confirmed based on the Triage C. difficile immunoassay and polymerase chain reaction assay to detect the presence of toxigenic C. difficile. The Fisher exact test was used to examine the relationship between categorical variables. A Cox regression model was used to estimate the risk of CDI development in IBD patients. RESULTS: Among 261 children with IBD, 20 (7.7%) developed CDI with an incidence rate of 5.04 cases per 1000 person-years, and the median age at diagnosis (interquartile range) was 12.96 (9.33-15.81) years. The incidence rates of CDI among UC and CD patients were 4.16 cases per 1000 person-years and 5.88 cases per 1000 person-years, respectively (P = 0.46). Compared with children without CDI, those who had CDI were at increased risk of future exposure to systemic corticosteroids (adjusted hazard ratio [aHR], 4.38; 95% confidence interval [CI], 1.46-13.10) and anti-tumor necrosis factor (anti-TNF) biologics (aHR, 3.31; 95% CI, 1.11-9.90). The recurrence rate of CDI in our pediatric IBD population was 25%. CONCLUSIONS: Our findings confirm that children with IBD are at high risk of developing CDI, which may predict future escalation of IBD therapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".