Declining Enrolment and Other Challenges in IBD Clinical Trials: Causes and Potential Solutions
Bibliographic record
Abstract
BACKGROUND: Rates of enrolment in clinical trials in inflammatory bowel disease [IBD] have decreased dramatically in recent years. This has led to delays, increased costs and failures to develop novel treatments. AIMS: The aim of this work is to describe the current bottlenecks of IBD clinical trial enrolment and propose solutions. METHODS: A taskforce comprising experienced IBD clinical trialists from academic centres and pharmaceutical companies involved in IBD clinical research predefined the four following levels: [1] study design, [2] investigative centre, [3] physician and [4] patient. At each level, the taskforce collectively explored the reasons for declining enrolment rates and generated an inventory of potential solutions. RESULTS: The main reasons identified included the overall increased demands for trials, the high screen failure rates, particularly in Crohn's disease, partly due to the lack of correlation between clinical and endoscopic activity, and the use of complicated endoscopic scoring systems not reflective of the totality of inflammation. In addition, complex trial protocols with restrictive eligibility criteria, increasing burden of procedures and administrative tasks enhance the need for qualified resources in study coordination. At the physician level, lack of dedicated time and training is crucial. From the patients' perspective, long washout periods from previous medications and protocol requirements not reflecting clinical practice, such as prolonged steroid management and placebo exposures, limit their participation in clinical trials. CONCLUSION: This joint effort is proposed as the basis for profound clinical trial transformation triggered by investigative centres, contract research organizations, sponsors and regulatory agencies.
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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 | 0.287 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".