Acute Severe Ulcerative Colitis: An International Delphi Consensus on Clinical Trial Design and Endpoints
Bibliographic record
Abstract
BACKGROUND & AIMS: Interventional clinical trials in acute severe ulcerative colitis (ASUC) are characterized by substantial heterogeneity due to a lack of consensus in several key areas of trial design-this impedes clinical research efforts to identify novel therapies. The objective of this initiative was to achieve the first consensus and provide clear position statements on ASUC trial design. METHODS: A modified Delphi consensus approach was employed with a panel of 20 clinicians with international representation and expertise in ASUC trial design and delivery. Agreement was defined as at least 75% of participants voting as "agree" with each statement. RESULTS: In total, 30 statements achieved consensus and were approved. Statements centred on proposing suitable eligibility criteria (disease extent, disease severity, prior therapy exposure), optimizing trial design (randomization, stratification, corticosteroid handling, timing of assessments), and recommending primary and secondary endpoints alongside defining key efficacy outcomes (clinical and endoscopic response and remission, treatment failure, quality of life). CONCLUSIONS: The expansion of drugs to treat moderate-severe ulcerative colitis over the past decade, particularly the rapidly acting Janus kinase inhibitors, is promising and has reignited the interest in identifying suitable therapeutic candidates for ASUC. Clinical trials in this high-risk population are challenging to conduct and this consensus provides a framework for future trials to advance drug development.
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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 | 0.665 | 0.442 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".