Histologic assessments in ulcerative colitis: the evidence behind a new endpoint in clinical trials
Bibliographic record
Abstract
INTRODUCTION: Treatment goals for ulcerative colitis (UC) are evolving from the achievement of clinical remission to more rigorous goals defined by endoscopic and histologic healing. Achievement of deeper remission targets aims to reduce the risk of colectomy, hospitalizations, and colorectal cancer. AREAS COVERED: This review covers histologic assessments, histologic remission as a clinical trial endpoint, and the association between histologic disease activity and clinical outcomes. Future directions are also discussed, including the use of advanced imaging and artificial intelligence technologies, as well as potential future treatment targets beyond histologic remission. EXPERT OPINION: Histologic assessments are used for their sensitivity in measuring mucosal inflammatory changes in UC. Due to correlation with disease activity, histologic assessments may support clinical decision-making regarding treatment decisions as such assessments can be associated with rates of clinical relapse, hospitalization, colectomy, and neoplasia. While histologic remission is limited by varying definitions and multiple histologic indices, work is ongoing to create a consensus on the use of histologic assessments in clinical trials. As research advances, aspirational targets beyond histologic remission, such as molecular healing and disease clearance, are being explored.
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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 | 0.080 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".