Recommendations for Broadening Eligibility Criteria in Inflammatory Bowel Disease Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Clinical trial recruitment for patients with inflammatory bowel disease (IBD) has become more challenging over time. We aimed to develop recommendations for broadening IBD clinical trial eligibility to improve the inclusion of a more representative patient population in a more efficient timeline. METHODS: We applied the RAND/UCLA Appropriateness Method focused on broadening IBD clinical trial eligibility. A literature review was performed for 7 domains, each representing a different area related to trial recruitment. Based on these domains, 32 statements were developed. A questionnaire was sent to IBD specialists to anonymously vote on each statement with regards to its appropriateness and feasibility. After the first round of voting, participants met for a moderated discussion to review all statements. At the end of the discussion a second round of anonymous voting led to the final recommendations. RESULTS: The final round of voting resulted in 26 statements. All were rated as feasible and 25 of 26 rated as appropriate. Recommendations generally are to be more inclusive of complicated disease phenotypes, more liberal around safety criteria, to recognize the importance of non-invasive imaging and biomarkers, to minimize the washout period and to not enforce a minimum or maximum number of prior medications, to allow a recently recorded colonoscopy to count as a baseline study, and to be less restrictive of age. CONCLUSION: Recommendations to broaden clinical trial eligibility were found to be both appropriate and feasible with a high degree of agreement amongst an international group of IBD specialists.
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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 | 0.622 | 0.718 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.036 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".