Efficacy and safety of advanced therapies for moderately to severely active ulcerative colitis in induction and maintenance: systematic literature review and Bayesian network meta-analysis
Bibliographic record
Abstract
Aim: Several therapies have recently been licensed for the treatment of patients with moderately to severely active ulcerative colitis (UC). To provide comparative evidence of newly available treatments, Bayesian network meta-analyses were conducted to compare their relative efficacy and safety profiles in both the induction and maintenance phases. Materials & methods: A systematic literature review was conducted to identify the available literature on randomized controlled trials for advanced treatments (AT) of moderately to severely active UC. Bayesian network meta-analyses were used to synthesize evidence on prespecified efficacy and safety outcomes. Primary efficacy end points clinical response and clinical remission were measured at the end of induction and clinical response and clinical remission among induction phase responders were assessed at the end of the maintenance period. Efficacy outcomes were analyzed separately for AT-naive and -experienced populations. Safety outcomes included serious infections over the induction period, and serious infections among others over the maintenance period. Treat-through trial outcomes were adjusted to align with responder rerandomized trial outcomes. Results: The systematic review identified 58 relevant trials of which 28 met criteria for inclusion in the main analysis networks. At the end of the induction period, all treatments were efficacious against placebo for both AT-naive and AT-experienced populations. Upadacitinib 45 mg demonstrated a higher likelihood of clinical response and remission compared with other treatments. Adalimumab had less favorable performance over the induction period. Among induction phase responders, most treatments demonstrated similar efficacy at the end of the maintenance period. Tofacitinib 10 mg was more likely to achieve clinical response and remission than several other treatments in the AT-naive population. In the AT-experienced population, upadacitinib 30 mg demonstrated a higher likelihood of clinical response and remission compared with other treatments. The safety outcomes among treatments were similar. Conclusion: This study provides an updated comparison of treatments for moderately to severely active UC. Most treatments demonstrated comparable efficacy at the end of maintenance. The findings from this study can inform decision making in treatment choice for patients with moderately to severely active UC.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".