Addition of Baseline Histology and Fecal Calprotectin Does Not Reduce Placebo Rates in Ulcerative Colitis Clinical Trials: Post-hoc Analysis of Patient Level Data
Bibliographic record
Abstract
BACKGROUND: Placebo response rates in ulcerative colitis (UC) trials are highly variable. It is uncertain whether adding objective measures of inflammation, such as fecal calprotectin (FC) or histologic activity, to conventional eligibility criteria could reduce placebo response and strengthen treatment effect estimates. This study evaluated whether applying baseline FC or histology thresholds would alter outcomes in UC clinical trials. METHODS: We conducted a post-hoc pooled analysis of individual patient-level data from five phase 3, randomized, placebo-controlled trials including 1918 patients on active therapy and 1149 on placebo. Baseline FC thresholds (>150, >200, >250, >500 µg/g) and Geboes histological thresholds (≥3.1, ≥3.2) were applied as hypothetical inclusion criteria. Outcomes assessed were post-induction clinical remission (CR: modified Mayo score with stool frequency ≤1 and ≥1-point decrease, rectal bleeding = 0, and endoscopic subscore ≤1) and endoscopic improvement (EI: endoscopic subscore ≤1). RESULTS: Applying FC or Geboes thresholds did not meaningfully reduce placebo response rates or increase treatment-placebo differences for CR or EI. For example, for vedolizumab, the CR difference vs placebo was 11% (95% CI: 3.5-18.5) in the unrestricted population and 10.4%-13% with thresholds applied, with up to 91 (33.6%) of participants excluded. For upadacitinib, EI differences were 36.2% (95% CI: 28.5-43.8) unrestricted and 35.9%-37.3% with restrictions, with up to 248 (38.7%) of participants excluded. Results were consistent across therapies and in subgroup analyses. CONCLUSION: Restricting trial enrollment based on elevated FC or histological activity did not meaningfully lower placebo response rates in phase 3 UC trials.
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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 | 0.203 | 0.221 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.024 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".