Systematic review with meta‐analysis: comparative efficacy of biologics for induction and maintenance of mucosal healing in Crohn's disease and ulcerative colitis controlled trials
Bibliographic record
Abstract
BACKGROUND: Mucosal healing is an important therapeutic endpoint in the management of Crohn's disease (CD) and ulcerative colitis (UC). Limited data exist regarding the comparative efficacy of various therapies in achieving this outcome. AIM: To perform a systematic review and meta-analysis of biologics for induction and maintenance of mucosal healing in Crohn's disease and ulcerative colitis. METHODS: We performed a systematic review and meta-analysis of randomised controlled trials (RCT) examining mucosal healing as an endpoint of immunosuppressives, anti-tumour necrosis factor α (anti-TNF) or anti-integrin monoclonal antibody therapy for moderate-to-severe CD or UC. Pooled effect sizes for induction and maintenance of mucosal healing were calculated and pairwise treatment comparisons evaluated using a Bayesian network meta-analysis. RESULTS: A total of 12 RCTs were included in the meta-analysis (CD - 2 induction, 4 maintenance; UC - 8 induction, 5 maintenance). Duration of follow-up was 6-12 weeks for induction and 32-54 weeks for maintenance trials. In CD, anti-TNFs were more effective than placebo for maintaining mucosal healing [28% vs. 1%, Odds ratio (OR) 19.71, 95% confidence interval (CI) 3.51-110.84]. In UC, anti-TNFs and anti-integrins were more effective than placebo for inducing (45% vs. 30%) and maintaining mucosal healing (33% vs. 18%). In network analysis, adalimumab therapy was inferior to infliximab [OR 0.45, 95% credible interval (CrI) 0.25-0.82] and combination infliximab-azathioprine (OR 0.32, 95% CrI 0.12-0.84) for inducing mucosal healing in UC. There was no statistically significant pairwise difference between vedolizumab and anti-TNF agents in UC. CONCLUSIONS: Anti-TNF and anti-integrin biological agents are effective in inducing mucosal healing in UC, with adalimumab being inferior to infliximab or combination therapy. Infliximab and adalimumab were similar in CD.
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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 | 0.032 | 0.080 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.032 | 0.053 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".