Considerations for Colorectal Neoplasia Detection in Inflammatory Bowel Disease Clinical Trials
Bibliographic record
Abstract
BACKGROUND: High-quality colonoscopic surveillance can lead to earlier and increased detection of colorectal neoplasia in patients with inflammatory bowel disease (IBD). In IBD clinical trials, endoscopy is used to assess mucosal disease activity before and after treatment but also provides an opportunity to surveil for colorectal neoplasia during follow-up. SUMMARY: Best practices for colorectal cancer identification in IBD clinical trials require engagement and collaboration between the clinical trial sponsor, site endoscopist and/or principal investigator, and central read team. Each team member has unique responsibilities for maximizing dysplasia detection in IBD trials. KEY MESSAGES: Sponsors should work in accordance with scientific guidelines to standardize imaging procedures, design the protocol to ensure the trial population is safeguarded, and oversee trial conduct. The site endoscopist should remain updated on best practices to tailor sponsor protocol-required procedures to patient needs, examine the mucosa for disease activity and potential dysplasia during all procedures, and provide optimal procedure videos for central read analysis. Central readers may detect dysplasia or colorectal cancer and a framework to report these findings to trial sponsors is essential. Synergistic relationships between all team members in IBD clinical trials provide an important opportunity for extended endoscopic evaluation and colorectal neoplasia identification.
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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 | 0.184 | 0.307 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| 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; 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".