Comparative efficacy of advanced treatments in biologic-naïve or biologic-experienced patients with ulcerative colitis: a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Only one head-to-head comparison of advanced treatments in moderately to severely active ulcerative colitis (UC) has been published; therefore, there remains a need for further comparisons. AIM: The relative treatment effects of filgotinib and adalimumab, golimumab, infliximab, tofacitinib, ustekinumab and vedolizumab were estimated using a network meta-analysis (NMA). METHOD: Systematically identified studies (MEDLINE, Embase and Cochrane Library; searched: inception-May 2019, updated November 2020) investigating treatments for moderately to severely active UC were re-evaluated for inclusion in a Bayesian NMA (fixed-effects model). Relative treatment effects were estimated using different permutations of patient population (biologic-naïve or biologic-experienced), treatment phase (induction or maintenance) and outcomes (MCS response/remission or endoscopic mucosal healing). RESULTS: Seventeen trials (13 induction; 9 maintenance) were included in the NMA; 8 treatment networks were constructed. Most targeted therapies were superior to placebo in terms of MCS response/remission and endoscopic mucosal healing; filgotinib 200 mg was similar to most other treatments. Infliximab 5 mg/kg was superior to filgotinib 200 mg (biologic-naïve; induction) for MCS response/remission (mean relative effect, 0.34 [95% credible interval: 0.05, 0.62]). Filgotinib 200 mg was superior to adalimumab 160/80/40 mg for MCS response/remission (biologic-experienced; induction; - 0.75 [- 1.16, - 0.35]), and endoscopic mucosal healing (biologic-naïve; maintenance; - 0.90 [- 1.89, - 0.01]); and to golimumab 50 mg every 4 weeks (biologic-naïve; maintenance; - 0.46 [- 0.94, 0]) for MCS response/remission. CONCLUSION: The current treatment landscape benefits patients with moderately to severely active UC, improving key outcomes; filgotinib 200 mg was similar to current standard of care in most outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".