Recent innovations in clinical trial design for inflammatory bowel disease
Bibliographic record
Abstract
Clinical trial design in inflammatory bowel disease (IBD) is evolving to address challenges in drug development and approvals. For clinical development, notable innovations include Bayesian designs, adaptive designs, integrated-phase trials and master protocols (such as umbrella, basket, and platform trials). The inclusion of biomarker-driven strategies and precision medicine (PM) trials bring aim to enable patient stratification based on prognostic or predictive markers, leveraging molecular signatures to customize therapy. However, recent studies highlight both the promise and complexity of this approach. Patient-reported outcomes (PROs) have gained prominence as key endpoints, aligning trials with patient-centric measures and regulatory guidance that emphasize symptoms and quality-of-life metrics. Digital health tools and artificial intelligence (AI) are being integrated to streamline trial conduct, from remote monitoring and telemedicine visits to AI-assisted recruitment and data analysis. Pragmatic trials and the integration of real-world evidence (RWE) aim to complement traditional efficacy trials by evaluating treatments in routine care settings. Together, these innovations mark a new era in IBD clinical trial design, aiming to expedite therapeutic development and enhance the relevance of trials to patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".