The Future of Clinical Trials in Inflammatory Bowel Disease
Bibliographic record
Abstract
The medical management of inflammatory bowel disease (IBD) has been transformed over the past few decades by the approval of multiple classes of advanced therapies and the integration of more targeted treatment strategies for Crohn's disease and ulcerative colitis. These changes have been driven by an increasing number of pivotal randomized controlled trials, which have grown in size and complexity over time. Several landmark studies that are anticipated to change current IBD management paradigms have recently been completed or are on-going, including the first head-to-head biologic trials, advanced combination treatment trials, therapeutic strategy and treatment target trials, and multiple phase 3 registrational programs of novel compounds. Despite these advances, the future of IBD trials also faces major challenges with respect to cost, feasibility, and recruitment. Accordingly, innovative methods for early and late phase randomized controlled trials must be adopted. In this review, we provide a comprehensive overview of the evolution of modern IBD trials, discuss methods for improving trial efficiency in early and late phase development, and provide insights into the interpretation and implications of these data for clinical care.
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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 | 0.441 | 0.582 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.020 | 0.031 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".