How Should an IBD Prevention Trial Be Designed?
Bibliographic record
Abstract
BACKGROUND: Advances in understanding disease pathogenesis have revealed a preclinical phase of inflammatory bowel disease (IBD), offering a potential window for preventive measures. There is growing interest in trials to prevent IBD in at-risk individuals. However, there is limited guidance on how to set up prevention trials in IBD. This review aims to outline key considerations for designing an IBD prevention trial. METHODS: We conducted a review of the literature, gaining insight from prevention trials in other immune-mediated inflammatory diseases (IMIDs). We focused on considerations to set up a secondary prevention trial regarding design, risk stratification and selection strategies, inclusion and exclusion criteria, endpoints, and ethical considerations. RESULTS: Across IMIDs in which features predictive of future risk have been identified, trials have leveraged well-characterized at-risk cohorts, biomarkers for disease prediction, and feasible interventions. Key elements to consider include (1) identification and longitudinal monitoring of at-risk individuals based on biomarkers, (2) clear definitions of inclusion and exclusion criteria distinguishing a primary prevention trial to prevent disease in at-risk individuals from a secondary prevention trial in individuals with signs of subclinical disease, (3) use of time-to-event endpoints, (4) risk-benefit balancing in intervention choice, and (5) engagement of at-risk individuals. In IBD, analogous strategies are emerging and first-degree relatives stand out as a group for screening. Significant challenges remain in defining risk thresholds, optimizing endpoints, and selected interventions. CONCLUSION: Prevention trials in IBD hold promise but require careful design informed by experiences from other IMIDs. Central to this effort are the development of validated predictive tools, ethically appropriate interventions, and international collaboration to assemble well-powered at-risk cohorts.
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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 | 0.320 | 0.503 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.022 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".