A Systematic Review of the Measurement of Endoscopic Healing in Ulcerative Colitis Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Assessment of endoscopic disease activity, as measured by various endoscopic evaluative instruments, is an essential part of quantifying disease activity in clinical trials in patients with ulcerative colitis (UC). Evaluative instruments have specific definitions and operating properties that influence the interpretation of clinical trial results. Our objective was to systematically review all endoscopic evaluative instruments that measure endoscopic disease activity in UC and to describe their definitions and operating characteristics (reliability, responsiveness, and predictive validity). METHODS: We performed a systematic review of evaluative instruments assessing endoscopic disease activity in UC. MEDLINE (Ovid), EMBASE (Ovid), PubMed, the Cochrane Library (CENTRAL), and Digestive Disease Week abstracts of clinical trials were searched from inception to January 2013. RESULTS: In total, 5885 studies were identified and screened for inclusion criteria. Four hundred twenty-two studies involving 31 evaluative instruments were identified. Two types of indices were found, numerical scoring systems and stepwise grading scales. CONCLUSIONS: Both the endoscopic evaluative instrument selected and the definition chosen for mucosal healing affect the validity of assessing endoscopic disease activity during a clinical trial for UC. Currently, the sigmoidoscopic component of the Mayo Score and the ulcerative colitis endoscopic index of severity show the most promise as reliable evaluative instruments of endoscopic disease activity. However, further validation is required.
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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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".