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 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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".