Integrating efficacy and safety of vedolizumab compared with other advanced therapies to assess net clinical benefit of ulcerative colitis treatments: a network meta-analysis
Bibliographic record
Abstract
Objectives: Because only one head-to-head randomized trial of biologics for moderate-to-severe UC has been performed, indirect treatment comparisons remain important. This systematic review and network meta-analysis examined efficacy and safety of biologics and tofacitinib for moderate-to-severe UC, using vedolizumab as reference.Methods: Relevant studies (N = 19) of vedolizumab, adalimumab, infliximab, golimumab, ustekinumab, and tofacitinib were identified. Study design differences were addressed by assessing efficacy outcomes conditional on response at maintenance initiation. Primary analysis used fixed-effect models to estimate odds ratios for efficacy and safety endpoints.Results: Compared with vedolizumab 300 mg, adalimumab 160/80 mg was associated with less clinical remission (odds ratio, 0.69 [95% credible interval, 0.54–0.88]), and infliximab 5 mg/kg was associated with more clinical remission (1.67 [1.16–2.42]) and response (1.63 [1.15–2.30]). Adalimumab 40 mg, golimumab 50 mg, and ustekinumab 90 mg Q12W had significantly lower clinical remission rates during maintenance (0.62 [0.45–0.86], 0.55 [0.32–0.95], and 0.59 [0.35–0.99]) versus vedolizumab 300 mg Q8W. Response results were similar. Tofacitinib 10 mg had the highest maintenance treatment efficacy estimates and highest infection risk.Conclusion: Network meta-analysis and novel integrated benefit-risk analysis suggest a potentially favorable efficacy-safety balance for vedolizumab vs adalimumab and other advanced UC therapies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| 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".