Real-World Characterization of Toxicities and Medication Management in Recipients of CAR T-Cell Therapy for Relapsed or Refractory Large B-Cell Lymphoma in Nova Scotia, Canada
Bibliographic record
Abstract
Nova Scotia (NS) began offering CAR T-cell therapy as a third-line standard of care for eligible patients with relapsed or refractory large B-cell lymphoma (r/r LBCL) in 2022. Recipients of CAR T-cell therapy often experience acute toxicities, including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), which require close monitoring and prompt management. This retrospective review aimed to describe the characteristics of adult patients with r/r LBCL deemed eligible to receive CAR T-cell therapy with axicabtagene ciloleucel in NS between January 2022 and June 2024, the toxicities experienced and toxicity management, hospital visits and intensive care unit (ICU) admissions, the utilization of toxicity management guidelines, and general efficacy outcomes. Twenty-seven patients received axicabtagene ciloleucel. All patients experienced CRS (7.4% grade ≥ 3), and 55.6% developed ICANS (25.9% grade ≥ 3). The median hospital stay was 18 days, with 40.7% requiring ICU admission. There was one treatment-related mortality. Most CRS (85.2%) and ICANS (80.0%) cases were managed according to the guidelines. By day +100, the best objective response rate was 81.5% (44.4% complete responses). Patients who received CAR T-cell therapy in NS, Canada, experienced comparable toxicities and efficacy to those reported in pivotal clinical trials and other real-world experiences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".