Clinical Course and Treatment Implications of Combination Immune Checkpoint Inhibitor-Mediated Hepatitis: A Multicentre Cohort
Bibliographic record
Abstract
BACKGROUND: Immune-related adverse events can occur after treatment with immune checkpoint inhibitors (ICI), limiting treatment persistence. We aimed to evaluate the clinical course of ICI-mediated hepatitis (IMH) associated with combination ipilimumab and nivolumab treatment. METHODS: A retrospective cohort study including consecutive patients with metastatic melanoma treated with ipilimumab and nivolumab between 2013 and 2018 was conducted at two tertiary care centres. IMH was defined by the Common Terminology Criteria for Adverse Events (CTCAE). We determined the proportion of patients developing IMH, and compared the duration, treatment patterns and outcomes, stratified by hepatitis severity. Kaplan-Meier survival analysis was used to evaluate time to hepatitis resolution, and a linear mixed-effects model was used to compare longitudinal outcomes by treatment. RESULTS: = 0.04). Ninety-four per cent (30/32) of patients had liver enzyme normalization after a median duration of 43 days (IQR 26 to 70 days). Corticosteroid use was not associated with faster IMH resolution or less ICI discontinuation. A total of 24 patients died during the study; no deaths were attributable to hepatitis-related complications. Fifty-three per cent (17/32) of patients resumed anti-PD-1 monotherapy and three patients developed IMH recurrence. CONCLUSIONS: Approximately half of the patients treated with combination ipilimumab and nivolumab developed IMH in this cohort. However, most patients experienced uncomplicated IMH resolution.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".