Comparative Efficacy of Biologics and Small Molecule Therapies in Improving Patient-Reported Outcomes in Ulcerative Colitis: Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Ulcerative colitis (UC) is a chronic disorder with a considerable negative impact on health-related quality of life (HRQoL), which has been recently recognized as an important treatment target. The purpose of this study is to compare the efficacy of different biologics and small molecule therapies in achieving better patient-reported outcomes and HRQoL in patients with UC. METHODS: We performed a systematic review and network meta-analysis of the EMBASE, MEDLINE, and Cochrane Central databases from inception until February 1, 2024. The primary endpoint was clinical remission in the patient-reported outcome (PRO-2) score in UC patients who were treated with different biologics or small molecules during induction and maintenance phases. PRO-2 score is the sum of both stool frequency and rectal bleeding subscores. The secondary outcome was improvement of HRQoL defined as an increase in Inflammatory Bowel Disease Questionnaire score of ≥16 points from baseline or any change in total score from baseline. A random effects model was used, and outcomes were reported as odds ratio with 95% confidence interval. Interventions were ranked per the SUCRA (surface under the cumulative ranking curve) score. RESULTS: A total of 54 studies were included in the primary outcome analysis and 15 studies were included in the secondary outcome analysis. The primary analysis showed that during the induction phase all of included drugs were better than placebo in improving the PRO-2 score. Interestingly, upadacitinib was found to be superior to most medications in improving PRO-2 scores. The secondary analysis showed that guselkumab ranked first in the improvement of the Inflammatory Bowel Disease Questionnaire score, followed by upadacitinib during the induction phase. CONCLUSION: Upadacitinib ranked first in PRO-2 clinical remission during the induction and maintenance phases. Guselkumab, mirikizumab, tofacitinib, and upadacitinib were the only novel medications that were superior to placebo in improving HRQoL in UC, with guselkumab ranking the highest, followed by tofacitinib and upadacitinib. During maintenance of remission, tofacitinib ranked highest in improving HRQoL.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".