Recommendations for the effective use of T-cell–redirecting therapies: a Canadian consensus statement
Bibliographic record
Abstract
Background: T-cell-redirecting therapies, such as bispecific antibodies and chimeric antigen receptor T-cells, exploit the cytotoxic capabilities of the immune system to destroy cells expressing specific surface antigens, including malignant cells. These therapies have demonstrated unprecedented rates, depth, and duration of responses in relapsed and refractory multiple myeloma. However, there are significant challenges in implementing these therapies into practice, which require multidisciplinary and multicenter coordination and significant healthcare resources to effectively manage these patients. So far, there are no Canadian guidelines for the effective implementation and use of T-cell-redirecting therapies. Methods: This consensus statement was developed based on three advisory meetings held in March, July, and November 2023. During these meetings, a panel of Canadian subject matter experts and representation from Myeloma Canada gathered to discuss the optimal procedures for the use of T-cell-redirecting therapies in the treatment of multiple myeloma. Members of the panel performed a thorough review of randomized clinical trials, real-world data, and other current literature, and provided their up-to-date clinical experience with T-cell-redirecting therapies in Canadian practice settings. Subsequently, asynchronous working groups were appointed to develop unified criteria for patient selection, appraise referral pathways, and devise strategies for management of short-term and long-term adverse events arising from the use of T-cell-redirecting therapies in multiple myeloma. Results: Here, we present recommendations for optimizing patient selection, referral pathways, and adverse event management in the Canadian practice setting. These recommendations are relevant for hematologists/oncologists, oncology nurses, pharmacists, nurse practitioners, physician assistants, and other providers who treat patients with multiple myeloma, as well as individuals with multiple myeloma and their care partners. These recommendations will be of interest to clinicians who treat patients with MM at community clinics and hospitals and who may be interested in referring patients for T-cell-redirecting therapy.
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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 | 0.102 | 0.139 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.015 | 0.007 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".