Management and Prevention of Cellular-Therapy-Related Toxicity: Early and Late Complications
Bibliographic record
Abstract
Chimeric Antigen Receptor T (CAR-T) cell therapy has dramatically changed prognosis and treatment of relapsed and refractory hematologic malignancies. Currently the 6 FDA approved products target various surface antigens. While CAR-T therapy achieves good response, life-threatening toxicities have been reported. Mechanistically, can be divided into two categories: (1) toxicities related to T-cell activation and release of high levels of cytokines: or (2) toxicities resulting from interaction between CAR and CAR targeted antigen expressed on non-malignant cells (i.e., on-target, off-tumor effects). Variations in conditioning therapies, co-stimulatory domains, CAR T-cell dose and anti-cytokine administration, pose a challenge in distinguishing cytokine mediated related toxicities from on-target, off-tumor toxicities. Timing, frequency, severity, as well as optimal management of CAR T-cell-related toxicities vary significantly between products and are likely to change as newer therapies become available. Currently the FDA approved CARs are targeted towards the B-cell malignancies however the future holds promise of expanding the target to solid tumor malignancies. Further highlighting the importance of early recognition and intervention for early and late onset CAR-T related toxicity. This contemporary review aims to describe presentation, grading and management of commonly encountered toxicities, short- and long-term complications, discuss preventive strategies and resource utilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".