The significance of histological activity measurements in immune checkpoint inhibitor colitis
Bibliographic record
Abstract
BACKGROUND: Colitis is a significant complication of immune checkpoint inhibitors (ICI). Currently, clinical and endoscopic severity are used to guide therapy. AIMS: To investigate associations between clinical, endoscopic, and histological features with outcomes METHODS: We identified 149 patients from seven institutions with biopsy-proven ICI colitis. Biopsies were evaluated for histological features including the Geboes score, and the Robarts histopathological index (RHI) was calculated. Clinical, endoscopic, and histological data were tested for associations with biological use and adverse colitis outcomes (biological-refractory colitis, colectomy or death from colitis). RESULTS: Three mutually exclusive histological patterns were identified: acute colitis, chronic active colitis and microscopic colitis. Microscopic colitis was associated with older age (68.5 vs 61 years for acute colitis pattern, P = 0.02) and longer time to colitis (5.5 vs 3 months for the other patterns, P = 0.05). Biological use was associated with earlier time to colitis (2 vs 3 months, P = 0.04) and higher RHI (18 vs 12, P = 0.007). On multivariate analysis, RHI ≥14 was associated with biological use with an odds ratio of 4.5 (95% CI 1.4-13.8; P = 0.01). Adverse colitis outcomes were associated with shorter time to colitis (2 vs 3 months, P = 0.008) and higher RHI (24 vs 14, P = 0.001). On multivariate analysis, RHI ≥24 was associated with adverse colitis outcomes with an odds ratio 9.5 (95% CI 2.1-42.3 P = 0.003). CONCLUSION: Histological activity as measured by RHI is the only factor independently associated with biological use and adverse colitis outcomes. Prospective studies are needed to validate these findings to determine if histological activity should be incorporated into therapeutic algorithms.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".