Recommendations for standardizing biopsy acquisition and histological assessment of immune checkpoint inhibitor-associated colitis
Bibliographic record
Abstract
Immune checkpoint inhibitor-associated colitis (ICIC) affects approximately 15% of cancer patients treated with immunotherapy. Although histological evaluation is potentially valuable for both the diagnosis of ICIC and evaluation of disease activity, use in clinical practice is heterogeneous. We aimed to develop expert recommendations to standardize histological assessment of disease activity in patients with ICIC. Using the modified Research and Development/University of California Los Angeles (RAND/UCLA) appropriateness methodology, an international panel of 11 pathologists rated the appropriateness of 99 statements on a 9-point Likert scale during two rounds of anonymous voting. Results were discussed between rounds using moderated videoconferences. There are currently no disease-specific instruments for assessing histological features of ICIC. The panel considered that colonoscopy with at least three biopsies per segment from a total of at least five segments, including both endoscopically normal and inflamed areas, was appropriate for tissue acquisition. They agreed that biopsies should be oriented such that the long axis of the colonic crypts is visualized and should be stained with hematoxylin and eosin. Histological items that the panel voted were appropriate to evaluate in ICIC included the degree of structural/architectural change, chronic inflammatory infiltrate, lamina propria and intraepithelial neutrophils, crypt abscesses and destruction, erosions/ulcerations, apoptosis, surface intraepithelial lymphocytosis, and subepithelial collagen thickness. The appropriateness of routine immunohistochemistry was uncertain. These expert recommendations will help standardize assessment of histological activity in patients with ICIC. The panel also identified the development and validation of an ICIC-specific histological index as a research priority.
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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 | 0.082 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".