The Most Recent Oncologic Emergency: What Emergency Physicians Need to Know About the Potential Complications of Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Immune checkpoint inhibitors targeting cytotoxic T-lymphocyte associated protein 4 (CTLA-4) and programmable cell death protein 1 (PD-1)/PD-L1 have shown antitumor activity in cancers such as melanoma, non-small cell lung cancer, renal cell carcinoma, and urothelial cancer. Certain checkpoint inhibitors have been approved for use in Canada, and are becoming a mainstay in the treatment of melanoma and other malignancies. These drugs have a unique side effect profile and are known to cause immune-related adverse events (irAEs). These adverse events often appear to originate from an infectious etiology, when in fact they result from the enhanced immune response caused by immune checkpoint therapy. IrAEs are primarily treated with corticosteroids, which suppress the overactive immune response that is secondary to the treatment. IrAEs can occur in any organ system, but adverse events in the skin, gastrointestinal, endocrine, and pulmonary systems are among the most common. As an emergency physician, one must be familiar with these drugs and their adverse events in order to identify patients presenting with irAE and treat them accordingly. This paper provides a brief introduction to immune checkpoint inhibitors, discusses the most common irAEs relevant to emergency physicians, and gives suggestions on how to manage patients presenting to the emergency department (ED) suffering from irAEs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".