Rapid Unmasking of Immune-related Adverse Events After Discontinuation of Chemotherapy in Chemo-immunotherapy Regimens
Bibliographic record
Abstract
Pembrolizumab is an immune-checkpoint inhibitor (ICI) of programmed cell death protein 1 (PD-1), which restores T-cell-mediated antitumor immune activity and therefore enhances the body's immune response to cancer cells. Due to the nature of this therapy, immune-related adverse events (irAE) can manifest in nearly every organ system. Chemo-immunotherapy regimens are now considered first-line treatment for several cancers, with recent literature suggesting there are higher rates of certain irAEs with ICI monotherapy when compared with chemo-immunotherapy combinations. In certain regimens chemo-immunotherapy induction is followed by ICI maintenance monotherapy, and data regarding irAE incidence in this transition period are very limited. We report 3 cases of patients on pembrolizumab in combination with cytotoxic chemotherapy who developed an irAE shortly following discontinuation of a chemotherapy agent. Cases were identified in the Rheumatology in Immuno-Oncology clinic at the University of Alberta and clinical data were extracted by retrospective chart review after obtaining written consent from individual patients. These findings demonstrate that chemotherapy may suppress irAEs in patients using ICIs, and that when chemotherapy agents in combined regimens are discontinued, irAEs can be "unmasked" within the following 6 weeks. Clinicians should be aware of this risk and monitor for irAE development during this critical time period. To the best of the authors' knowledge, this has not been previously reported in the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".