Recurrence of Clostridium difficile Infection in Patients with Inflammatory Bowel Disease: The RECIDIVISM Study
Bibliographic record
Abstract
OBJECTIVES: Recurrent Clostridium difficile infection (rCDI) contributes to a significant burden of disease in patients with inflammatory bowel disease (IBD). In this study, we seek to identify risk factors for rCDI in a population of IBD patients at the Mount Sinai Hospital IBD Centre. METHODS: In this retrospective cohort study, IBD patients with rCDI diagnosed between 2010 and 2013 were identified and compared with IBD patients with single-episode CDI. Multivariate regression was used to identify predictors of rCDI in IBD. Outcome analysis was performed for hospitalizations due to CDI, colectomy, and CDI-attributable mortality. RESULTS: A total of 503 patients were included, 110 (22%) of whom had IBD (49% CD, 51% ulcerative colitis). Recurrent CDI occurred in 32% of IBD patients compared with 24% of non-IBD patients (P<0.01). IBD patients with rCDI were more likely than those without rCDI to report recent antibiotic therapy (42.9 vs. 30.7%, P<0.01), 5-aminosalicylic acid (5-ASA) use (51.5 vs. 30.7%, P<0.001), steroid use (51.4 vs. 33.3%, P<0.001), and biologic therapy (48.6 vs. 40.0%, P<0.01). Infliximab (34.3 vs. 17.3%, P<0.01) but not adalimumab was associated with more rCDI events. Using a Cox model of predictors of rCDI in IBD, significant predictors included non-ileal Crohn's disease (hazard ratio (HR) 2.85, 95% confidence interval (CI) 1.30-6.30) and the use of 5-ASA (HR 2.15, 95% CI 1.11-4.18). CONCLUSIONS: Compared with the general population, IBD patients are 33% more likely to experience rCDI. Within the IBD cohort, exposure to certain drug classes (antibiotics, 5-ASA, steroids, certain biologics) and non-ileal Crohn's disease were found to be the predictors of rCDI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".