Review of CAR T-Cell Therapy in Multiple Myeloma: A Canadian Perspective
Bibliographic record
Abstract
Multiple myeloma (MM) is an incurable plasma cell malignancy. In the context of the current standard of care therapies in Canada, outcomes among patients with relapsed/refractory multiple myeloma (RRMM), particularly those with triple-class (or more) refractory disease remain poor. Immunotherapies have significantly changed the treatment landscape of MM. Since 2021, two BCMA-targeting CAR T-cell therapy products have been approved for RRMM—namely Idecabtagene vicleucel (Ide-cel) (ABECMA®) and Ciltacabtagene autoleucel (Cilta-cel) (CARVYKTI®), both of which are available in the US and Europe. Although they have shown unprecedented efficacy in RRMM, their clinical and logistical limitations must be acknowledged. MM CAR T-cell therapy is likely to be approved in Canada soon. Therefore, it is timely that we review the latest evidence for commercially available CAR T-cell therapy in multiple myeloma, with a focus on its relevance and impact in the Canadian setting. There will be challenges to access and strategies must be in place to ensure equitable care for all Canadians with MM. Alongside haematologists working in the immune effector cell therapy programs, providers in the community will also play a role in the ongoing monitoring and management of long-term side effects including opportunistic infections and late neurotoxicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".