Vedolizumab does not increase risk of clostridium difficile infection in patients with inflammatory bowel disease using vedolizumab: A retrospective cohort study
Bibliographic record
Abstract
Introduction: infection (CDI) over the last decade. Patients with inflammatory bowel disease (IBD) who develop CDI are more prone to morbidity and mortality than CDI in patients without IBD. This study seeks to evaluate whether IBD patients who use vedolizumab are at increased risk of CDI compared to IBD patients using other therapies. Methods: This was a retrospective cohort study, and 684 patients with confirmed IBD (228 on vedolizumab, 228 on anti-TNF, and 228 on 5- Aminosalicylates acid therapy) were enrolled from January 2009 to August 2019 at a tertiary referral IBD center at McMaster University Medical Centre (MUMC) in Hamilton, Ontario, Canada. The primary outcome was time to the development of CDI in IBD patients using different therapies. Secondary outcomes included rates of CDI and the association between baseline variables and risk of CDI. A Cox proportional hazards (PH) model was used to evaluate baseline factors and development of CDI. Result: There was no difference in time to CDI between the three treatment groups (log rank p-value 0.37). CDI occurred in 16 patients (2.3%), specifically four patients (1.75%) in the vedolizumab group, four patients (1.75%) in the anti-TNF group, and eight patients (3.5%) in the 5-ASA group. The Cox PH model found current smoking, older age, and concomitant immunomodulator use as risk factors for CDI, after adjustment for other covariates. Vedolizumab was not associated with increased risk of CDI in the model. Conclusion: Risk factors for CDI in IBD patients included smoking, older age at the onset of medication, and immunomodulator therapy. Clinicians should have high degree of suspicion for CDI in IBD patients presenting with diarrhea, particularly in those with risk factors identified in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".