Systematic review: disease activity indices for immune checkpoint inhibitor‐associated enterocolitis
Bibliographic record
Abstract
BACKGROUND: Although there is interest in developing pharmacotherapies for the treatment of immune checkpoint inhibitor-associated enterocolitis (ICIC), there is currently no consensus on how to optimally measure disease activity in this condition. AIMS: To identify all scoring indices used for the measurement of disease activity in ICIC, assess their operating properties, and explore their potential utility as outcome measures. METHODS: We searched MEDLINE, EMBASE and the Cochrane Library from inception to November 2020 to identify studies that evaluated disease activity and severity in patients with ICI-associated enterocolitis. These scoring tools could be designed specifically for ICIC or adapted from other diseases, and assessed clinical, endoscopic, or histologic disease activity. RESULTS: Sixty-four studies were included. The Common Terminology Criteria for Adverse Events is commonly used to describe symptoms, although has only been partially validated and was not designed as a disease activity index. Endoscopic and histologic indices used in inflammatory bowel disease have been adopted for ICIC including the Mayo Endoscopic Subscore, Ulcerative Colitis Endoscopic Index of Severity, Simple Endoscopic Score for Crohn's Disease, Nancy Histological Index, Robarts Histopathological Index, and Geboes Score, among others. None of these indices has been validated for use in ICIC, and all lacked content validity and responsiveness. CONCLUSIONS: There are no validated clinical, endoscopic, or histologic outcomes to assess disease activity in ICIC. Development and validation of reliable and responsive outcome measures that can be used to measure disease activity will be paramount for both clinical practice and for the development of treatments.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".