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 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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.054 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".