Network Meta‐Analysis: Histologic and Histo‐Endoscopic Improvement and Remission With Advanced Therapy in Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: Histology has prognostic value in ulcerative colitis (UC). However, direct comparisons of histological endpoints are lacking. AIM: To perform a network meta-analysis (NMA) to compare histological endpoints with biologics and small molecules. METHODS: We searched four databases up until July 2024 for randomised controlled trials (RCTs) on advanced therapies for moderate-to-severe UC reporting histological endpoints. Outcomes included histological improvement or remission, and histo-endoscopic improvement after induction or during maintenance. We used a random-effects frequentist model and have reported outcomes as relative risk and 95% confidence interval. We estimated relative drug efficacy with the P-score. We conducted subgroup analysis by trial phase and evaluated risk of bias and evidence certainty. RESULTS: We included 24 RCTs (15 therapies, 8874 patients). Nineteen provided data on induction and 10 on maintenance; outcome definitions were similar. Etrasimod 2 mg/day ranked highest in achieving histologic improvement (P-score 0.98) and remission (P-score 0.90) following induction. Globally, guselkumab 200-400 mg ranked first for histo-endoscopic improvement, while etrasimod 2 mg/day and upadacitinib 45 mg/day were superior in the subgroup analysis. During maintenance, upadacitinib 30 mg/day was superior in achieving histologic improvement and remission (P-score 0.88 for both) and histo-endoscopic improvement (P-score 0.94). Etrasimod 2 mg/day ranked second for histologic remission (P-score 0.70) and histo-endoscopic improvement (P-score 0.73), while mirikizumab 200 mg/month ranked second for histologic improvement. CONCLUSION: These results support the ability of small molecules to achieve stringent endpoints in moderate-to-severe UC. Histological outcome data for biologics was sparser, particularly during maintenance. Head-to-head RCTs are imperative to better inform clinical practice.
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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.042 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".