An update on the safety of long-term vedolizumab use in inflammatory bowel disease
Bibliographic record
Abstract
INTRODUCTION: Vedolizumab (Entyvio) is a humanized monoclonal antibody that disrupts the interaction between α4β7 integrin on circulating T-lymphocytes and MAdCAM-1 on the vascular endothelium to prevent their egress to sites of gut inflammation. It has proven therapeutic efficacy for the treatment of moderate-to-severe Crohn's disease, ulcerative colitis, and pouchitis. AREAS COVERED: This narrative review assesses the safety profile of vedolizumab from the registration trial programs, open-label extension studies, observational real-world data, and pooled safety analyses. This includes an evaluation of the long-term overall safety in special populations typically underrepresented in clinical trials. EXPERT OPINION: Vedolizumab is an effective therapy for inflammatory bowel disease with a well-established safety profile. No unexpected long-term safety signals have been identified. Safety data in pregnancy, in pediatric and elderly populations, in patients undergoing surgery, and in patients with a prior history of cancer are reassuring. Due to its safety merits, we propose that vedolizumab is an excellent candidate for advanced combination treatment with an anti-cytokine approach using another biologic or novel small molecule inhibitor. This is important in patients with medically refractory IBD, in patients at high risk of developing disease-related complications, or in patients with concomitant uncontrolled immune-mediated inflammatory diseases.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".