Real World Outcomes and Hepatotoxicity of Infliximab in the Treatment of Steroid-Refractory Immune-Related Adverse Events
Bibliographic record
Abstract
BACKGROUND AND AIMS: Current guidelines state that infliximab is contraindicated for the treatment of immune checkpoint inhibitor-related hepatitis (ir-hepatitis) due to the risk of inducing further liver damage. As this recommendation is largely based on the use of infliximab for rheumatologic diseases, we evaluated the efficacy and hepatotoxicity of infliximab in patients with steroid-refractory immune-related adverse events (irAEs). METHODS: We retrospectively reviewed consecutive patients treated with infliximab for irAEs at Princess Margaret Cancer Centre. To assess hepatotoxicity, we compared the mean value of ALT, AST, and total bilirubin (BT) before and after infliximab treatment. We used logistic regression to assess factors associated with infliximab efficacy. RESULTS: Between January 2010 and February 2019, 56 patients were identified. The median age of the patients was 63 (27-84) years. Colitis was the most frequent toxicity (66%), followed by pneumonitis (11%). Infliximab was used to treat ir-hepatitis in one patient. The median number of infliximab doses was 1 (1-3) and led to toxicity resolution in 43 (76%) patients. The mean ALT, AST, and BT levels before and after infliximab treatment were not statistically different. The patient treated for ir-hepatitis had a complete recovery, with no incremental liver toxicity. CONCLUSIONS: In this dose-limited setting, infliximab was effective in resolving irAEs and did not induce hepatotoxicity.
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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 | 0.009 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".