Prospective Evaluation of Endoscopic and Histologic Indices in Pediatric Ulcerative Colitis Using Centralized Review
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate the reliability and validity of the Ulcerative Colitis (UC) Endoscopic Index of Severity (UCEIS) and Mayo Endoscopy Score (MES) and to validate the Robarts Histopathology Index (RHI) and Nancy Index (NI) in pediatric UC. We examined rectosigmoid and pancolonic versions of each instrument. METHODS: Single-center cross-sectional study of 60 prospectively enrolled participants. Through central endoscopy review, 4 pediatric gastroenterologists assigned rectosigmoid and pancolonic (mean of 5 colonic segments) UCEIS and MES scores. Two blinded pathologists assigned rectosigmoid and pancolonic RHI and NI scores. We assessed reliability with intraclass correlation coefficients and weighted kappa statistics and explored construct validity with correlations, boxplots, and receiver operator characteristic curves. RESULTS: The UCEIS and MES displayed almost perfect intra-rater and inter-rater reliability (intraclass correlation coefficient and weighted kappa ≥0.85), moderate-to-strong correlation with histologic/clinical activity and fecal calprotectin (FC), and very strong correlation with global endoscopic severity (r > 0.9). Rectosigmoid UCEIS and MES scores of 0 were highly specific (≥95%) for endoscopic and histologic remission throughout the colon. Pancolonic endoscopy scores correlated more strongly with histologic activity, clinical activity, and systemic inflammatory markers and better discriminated between degrees of active disease. RHI and NI showed moderate-to-strong correlation (r = 0.5-0.83) with endoscopic/clinical activity and FC. DISCUSSION: Our findings support the reliability and construct validity of the UCEIS and MES and the construct validity of the RHI and NI in pediatric UC. Normal rectosigmoid findings predicted pancolonic healing, but, given active disease, pancolonic endoscopic assessment more accurately captured global disease burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".