Management of the Critically Ill Adult Chimeric Antigen Receptor-T Cell Therapy Patient: A Critical Care Perspective
Bibliographic record
Abstract
OBJECTIVES: Chimeric antigen receptor T-cell therapy, a type of immune effector therapy for cancer, has demonstrated encouraging results in clinical trials for the treatment of patients with refractory hematologic malignancies. Nevertheless, there are toxicities specific to these treatments that, if not recognized and treated appropriately, can lead to multiple organ failure and death. This article is a comprehensive review of the available literature and provides, from a critical care perspective, recommendations by experienced intensivists in the care of critically ill adult chimeric antigen receptor T-cell patients. DATA SOURCES: PubMed and Medline search of articles published from 2006 to date. STUDY SELECTION: Clinical studies, reviews, or guidelines were selected and reviewed by the authors. DATA EXTRACTION: Not available. DATA SYNTHESIS: Not available. CONCLUSIONS: Until modifications in chimeric antigen receptor T-cell therapy decrease their toxicities, the intensivist will play a leading role in the management of critically ill chimeric antigen receptor T-cell patients. As this novel immunotherapeutic approach becomes widely available, all critical care clinicians need to be familiar with the recognition and management of complications associated with this treatment.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".