Critical Care Management of Toxicities Associated With Targeted Agents and Immunotherapies for Cancer
Bibliographic record
Abstract
OBJECTIVES: To describe the most common serious adverse effects and organ toxicities associated with emerging therapies for cancer that may necessitate admission to the ICU. DATA SOURCES AND STUDY SELECTION: PubMed and Medline search of relevant articles in English on the management of adverse effects of immunotherapy for cancer. DATA EXTRACTION AND DATA SYNTHESIS: Targeted therapies including tyrosine kinase inhibitors, monoclonal antibodies, checkpoint inhibitors, and immune effector cell therapy have improved the outcome and quality of life of patients with cancer. However, severe and life-threatening side effects can occur. These toxicities include infusion or hypersensitivity reactions, cytokine release syndrome, pulmonary, cardiac, renal, hepatic, and neurologic toxicities, hemophagocytic lymphohistiocytosis, opportunistic infections, and endocrinopathies. Cytokine release syndrome is the most common serious toxicity after administration of monoclonal antibodies and immune effector cell therapies. Most of the adverse events from immunotherapy results from an exaggerated T-cell response directed against normal tissue, resulting in the generation of high levels of proinflammatory cytokines. Toxicities from targeted therapies are usually secondary to "on target toxicities." Management is largely supportive and may include discontinuation of the specific agent, corticosteroids, and other immune suppressing agents for severe (grade 3 or 4) immune-related adverse events like neurotoxicity and pneumonitis. CONCLUSIONS: The complexity of toxicities associated with modern targeted and immunotherapeutic agents for cancer require a multidisciplinary approach among ICU staff, oncologists, and organ specialists and adoption of standardized treatment protocols to ensure the best possible patient outcomes.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".