A167 HISTOLOGICAL REMISSION PLACEBO RATES IN ULCERATIVE COLITIS TRIALS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background High histologic remission rates have been reported with placebos in randomized controlled trials (RCTs) evaluating ulcerative colitis (UC) therapies and have varied based on trial designs. We performed a systematic review and meta-analysis to quantify placebo histological remission rates and identify factors influencing those rates. Purpose This systematic review aims to improve future trials design and minimize placebo rates in UC trials. Method MEDLINE, EMBASE, and the Cochrane library were searched from inception of the databases until December 2021. We included placebo-controlled RCTs of adult patients with UC treated with aminosalicylates, corticosteroids, immunosuppressives, biologics, and small molecules. We pooled estimates using a random-effects model and performed subgroup analysis as well as meta-regression to evaluate the effect of different covariates on placebo rates. Result(s) Thirty-three studies (30 induction and 3 maintenance) were included. The overall placebo histological remission rate was 15.7% [95% CI 12.9-19%] across all 33 studies (Figure). High heterogeneity was observed among studies with I2 = 62.10%. In induction studies, the pooled estimate of histological remission was 15.8% [95% CI 12.7-19.5%], while in maintenance studies the pooled estimate was 14.5% [95% CI 8.4-24%]. Subgroup analysis revealed statistically significant differences in placebo rates when accounting for background medications, the intervention drug class, and disease severity [p= 0.041, 0.025, and 0.025, respectively]. There was no statistical difference between induction vs. maintenance studies or between different histological scales [p= 0.771, and 0.075, respectively]. Meta-regression showed similar results except that the therapy used was not statistically significant [p-value= 0.059]. Image Conclusion(s) Placebo histological remission rates range from 13-19% in UC RCTs, but studies are highly heterogeneous. Factors found to influence placebo rates include presence of background medications, the drug used and the disease severity in UC patients. These observations have important implications in informing future trial designs to minimize placebo rates and reduce heterogeneity. Disclosure of Interest M. Youssef: None Declared, K. Dong: None Declared, S. J. Lee: None Declared, N. Narula Speakers bureau of: received honoraria from Janssen, Abbvie, Takeda, Pfizer, Merck, Sandoz, Novartis, and Ferring
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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 | 0.038 | 0.083 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.054 |
| Bibliometrics | 0.013 | 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 | 0.002 |
| 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".