Efficacy and Safety of Advanced Therapies for Moderately to Severely Active Ulcerative Colitis at Induction and Maintenance: An Indirect Treatment Comparison Using Bayesian Network Meta-analysis
Bibliographic record
Abstract
Background: Given rapid innovation in advanced therapies for moderately to severely active ulcerative colitis (UC), we investigated their comparative efficacy and safety during induction and maintenance through network meta-analysis. Methods: Using Bayesian methods, endpoints of clinical remission and clinical response per Full Mayo score, and endoscopic improvement were assessed in bio-naive and -exposed populations. Safety was assessed in overall populations by all adverse events (AEs), serious AEs, discontinuation due to AEs, and serious infections. Phase 3 randomized controlled trials were identified via systematic literature review, including the following advanced therapies: infliximab, adalimumab, vedolizumab, golimumab, tofacitinib, ustekinumab, filgotinib, ozanimod, and upadacitinib. Random effects models were used to address between-study heterogeneity. Intent-to-treat (ITT) efficacy rates were calculated by adjusting maintenance outcomes by likelihood of induction response. Results: Out of 48 trials identified, 23 were included. Across all outcomes and regardless of prior biologic exposure, ITT efficacy rates were highest for upadacitinib, owing to its highest ranking for all efficacy outcomes in induction and for all but clinical remission during maintenance among bio-naive induction responders. For all advanced therapies versus placebo, there were no significant differences in serious AEs or serious infections across therapies. For all AEs, golimumab had higher odds versus placebo during maintenance; for discontinuation due to AEs, upadacitinib had lower odds versus placebo during induction, while ustekinumab and vedolizumab had lower odds versus placebo during maintenance. Conclusions: Upadacitinib may be the most efficacious therapy for moderately to severely active UC based on ITT analyses, with similar safety across advanced 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 | 0.043 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.043 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".