Respiratory Tract Infections in Patients With Inflammatory Bowel Disease: Safety Analyses From Vedolizumab Clinical Trials
Bibliographic record
Abstract
BACKGROUND AND AIMS: Vedolizumab, a humanised monoclonal antibody for the treatment of inflammatory bowel disease, selectively blocks gut lymphocyte trafficking. This may reduce the risk of respiratory tract infections [RTIs] compared with systemic immunosuppressive therapies. To assess this possibility, we evaluated the rates of RTIs in clinical trials of vedolizumab. METHODS: Patient-level data from Phase 3 randomised controlled trials [RCTs] of vedolizumab in ulcerative colitis [UC; GEMINI 1] and Crohn's disease [CD; GEMINI 2], and a long-term safety study [UC and CD] were pooled. Cox proportional hazards models were used to estimate the incidence of upper RTIs [URTIs] and lower RTIs [LRTIs] with adjustment for significant covariates. RESULTS: In the RCTs [n = 1731 patients], the incidence of URTIs was numerically higher in patients receiving vedolizumab compared with those receiving placebo, although this difference was not statistically significant (38.7 vs 33.0 patients per 100 patient-years; hazard ratio [HR] 1.12; 95% confidence interval [CI]: 0.83-1.51; p = 0.463). The rate of LRTIs, including pneumonia, was numerically lower in the vedolizumab versus the placebo group: this difference was not statistically significant (7.7 vs 8.5 per 100 patient-years [HR 0.85; 95% CI: 0.48-1.52; p = 0.585]). Both URTIs and LRTIs were more frequent in patients with CD compared with UC. Most RTIs in patients receiving vedolizumab were not serious and did not require treatment discontinuation. CONCLUSIONS: Vedolizumab therapy was not associated with an increased incidence of respiratory tract infection compared with placebo.
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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 | 0.056 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".