Cytokine release syndrome after CAR T-cell therapy for B-cell acute lymphoblastic leukemia in children and young adolescents: storms make trees take deeper roots
Bibliographic record
Abstract
INTRODUCTION: Chimeric antigen receptor (CAR) T-cells have revolutionized cancer treatment, showing significant success, including treatment of pediatric B-cell acute lymphoblastic leukemia (B-ALL). Despite their efficacy, cytokine release syndrome (CRS) emerges as a common early adverse effect that can be life threatening in severe cases, resulting from the immune system's targeted activation against tumors. AREAS COVERED: This review concentrates on CRS in children and young adults undergoing CAR T-cell therapy for B-ALL. It explores CRS pathophysiology, clinical presentation, and incidence, emphasizing the importance of a consensus definition and grading to homogenize the treatment according to the severity of symptoms. We will discuss the standard of care treatment of CRS but also novel approaches. We will highlight the importance of managing CRS without compromising the efficacy of immune cell activation against tumors. EXPERT OPINION: As CAR T-cell therapy in pediatric B-ALL become increasingly available and used, optimal management of CRS becomes increasingly important. Early recognition and timely management has improved. Further information will aid us to identify optimal timing of tocilizumab and corticosteroids. Continued bench research coupled with clinical studies and biomarker discovery will allow for valuable insights into CRS pathophysiology and patient and/or cell-targeted treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".