Scoring indices for assessing endoscopic disease activity in acute severe ulcerative colitis: a systematic review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Endoscopy is important for assessing disease severity and potentially predicting treatment response in acute severe ulcerative colitis (ASUC). We aimed to identify and determine the operating properties of existing endoscopic indices/items used to assess disease activity in ASUC. METHODS: MEDLINE, Embase, and Cochrane CENTRAL were searched from database inception to April 17, 2024 to identify individual items and scoring indices used to evaluate endoscopic disease activity in patients with ASUC. Subsequently, we performed another comprehensive search from database inception to July 29, 2024 to identify studies that assessed the validity, reliability, feasibility, and responsiveness of the identified items and scoring indices. RESULTS: We identified 18 studies that reported endoscopic measures in patients with ASUC, including Endoscopic Activity Index, Mayo endoscopic subscore (MES), Severe Endoscopic Lesions, Ulcerative Colitis Endoscopic Index of Severity (UCEIS), and the Degree of Ulcerative Colitis Burden of Luminal Inflammation (DUBLIN) score or sub-components of these indices. A total of 33 studies evaluated the operating properties of the MES, UCEIS, and DUBLIN score in ASUC. The MES and the UCEIS demonstrated adequate discriminant construct validity, convergent construct validity, and responsiveness. Feasibility or reliability were not assessed for these scores. The DUBLIN score demonstrated indeterminate discriminant construct validity and convergent construct validity with limited data. Responsiveness, feasibility, and reliability were not assessed for this score. CONCLUSIONS: These results highlight the need for a validated endoscopic score that can accurately describe and quantify the severity of endoscopic lesions and potentially predict outcomes in ASUC patients.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".