A real-world, long-term experience on effectiveness and safety of vedolizumab in adult patients with inflammatory bowel disease: The Cross Pennine study
Bibliographic record
Abstract
BACKGROUND: Real-life data on vedolizumab effectiveness in inflammatory bowel disease (IBD) are still emerging. Data on the comparative safety of the gut selective profile are of particular interest. AIMS: To assess clinical outcome and safety in IBD patients treated with vedolizumab. METHODS: We retrospectively collected data of patients treated with vedolizumab at eight UK hospitals (August 2014-January 2018). Clinical response and remission at 14 and 52 weeks evaluated through Physician Global Assessment (PGA) and adverse events were recorded. Possible predictors of clinical response were examined. RESULTS: Two hundred and three IBD patients (mean treatment 16 ± 8 months) were included. Of these, 135 patients (mean age 40.6 ± 16.0 years; F:M 1.9:1) had CD and 68 (mean age 44.5 ± 18.1 years; F:M 1:1.2) had UC. According to PGA, 106/135 (78.5%) CD and 62/68 (91.2%) UC patients (p = 0.02) had a clinical response/remission at 14 weeks, whereas 76/119 (63.9%) CD and 52/63 (82.5%) UC patients (p < 0.01) showed a sustained response or remission at 52 weeks, with a high adherence rate (97%). No predictors of clinical response were found. The cumulative incidence of infectious diseases was 11.9 per 100 person-years. CONCLUSION: Vedolizumab is an effective therapy for inducing and maintaining remission of IBD, with better results for UC, and with a good safety profile.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".