Mechanisms of Immune Checkpoint Inhibitor-Mediated Colitis
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) have provided tremendous clinical benefit in several cancer types. However, systemic activation of the immune system also leads to several immune-related adverse events. Of these, ICI-mediated colitis (IMC) occurs frequently and is the one with the highest absolute fatality. To improve current treatment strategies, it is important to understand the cellular mechanisms that induce this form of colitis. In this review, we discuss important pathways that are altered in IMC in mouse models and in human colon biopsy samples. This reveals a complex interplay between several types of immune cells and the gut microbiome. In addition to a mechanistic understanding, patients at risk should be identifiable before ICI therapy. Here we propose to focus on T-cell subsets that interact with bacteria after inducing epithelial damage. Especially, intestinal resident immune cells are of interest. This may lead to a better understanding of IMC and provides opportunities for prevention and management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".