Expert clinical framework report: Management of adverse events related to novel therapies for relapsed/refractory multiple myeloma (RRMM)
Bibliographic record
Abstract
Multiple Myeloma (MM) is a malignancy of the plasma cells accumulating in the bone marrow. MM develops stepwise from the premalignant conditions, monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). The Canadian Cancer Society estimates that in 2022 4,000 Canadians will be diagnosed, and 1,650 will die from MM. Survival rates have improved over the years with the development of novel treatment strategies, including proteasome inhibitors (PI), immunomodulatory drugs (IMiDs), targeted antibody and cellular therapies, and a selective inhibitor of nuclear export (SINE), as well as with the use of combinations of drugs. Although a number of patients will have a durable response following high-dose chemotherapy and autologous stem cell transplant (ASCT), MM remains an incurable malignancy with the majority of patients relapsing and eventually developing refractory disease (RRMM). Collaborative environments, in which pharmacists work with hematologists/oncologists, nurse practitioners, and supportive care teams, have been shown to improve adherence to the treatment plan. Prescription of appropriate prophylaxis in combination with various treatment strategies may reduce the number and duration of treatment delays. Intensified clinical and pharmaceutical care, including medication management and structured patient counseling for patients on oral anticancer drugs, has been shown to reduce the number of medication errors and severe side effects while improving the patient’s treatment experience. Nurses play a vital role in the management of toxicities as they educate, support, and advocate for patients. This report discusses the management of adverse events (AEs) related to both established agents and novel therapies for the optimal management of patients with RRMM. Established and novel therapies are often used in combination, which presents the potential for overlapping toxicities. The optimal combination therapies including the sequencing of various regimens are yet to be determined. Basic research and clinical trials with investigational agents are ongoing in an effort to improve both the depth and duration of response in newly diagnosed patients and those with RRMM with the aim of finding the best treatment options for every patient.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.084 | 0.040 |
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".