Immune-Related Adverse Events Associated With Immune Checkpoint Inhibitor Therapy
Bibliographic record
Abstract
As part of immune surveillance, killer T lymphocytes search for cancer cells and destroy them. Some cancer cells, however, develop escape mechanisms to evade detection and destruction. One of these mechanisms is the expression of cell surface proteins which allow the cancer cell to bind to proteins on T cells called checkpoints to switch off and effectively evade T-cell-mediated destruction. Immune checkpoint inhibitors (ICIs) are antibodies that block the binding of cancer cell proteins to T-cell checkpoints, preventing the T-cell response from being turned off by cancer cells and enabling killer T cells to attack. In other words, ICIs restore innate antitumor immunity, as opposed to traditional chemotherapies that directly kill cancer cells. Given their relatively excellent risk-benefit ratio when compared to other forms of cancer treatment modalities, ICIs are now becoming ubiquitous and have revolutionized the treatment of many types of cancer. Indeed, the prognosis of some patients is so much improved that the threshold for admission for intensive care should be adjusted accordingly. Nevertheless, by modulating immune checkpoint activity, ICIs can disrupt the intricate homeostasis between inhibition and stimulation of immune response, leading to decreased immune self-tolerance and, ultimately, autoimmune complications. These immune-related adverse events (IRAEs) may virtually affect all body systems. Multiple IRAEs are common and may range from mild to life-threatening. Management requires a multidisciplinary approach and consists mainly of immunosuppression, cessation or postponement of ICI treatment, and supportive therapy, which may require surgical intervention and/or intensive care. We herein review the current literature surrounding IRAEs of interest to anesthesiologists and intensivists. With proper care, fatality (0.3%-1.3%) is rare.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".