Practical Considerations for Early Relapsed/Refractory Multiple Myeloma in the Canadian Landscape in 2024
Bibliographic record
Abstract
Multiple-class drug combinations have long been integral to the management of multiple myeloma (MM). This has led to significant advances in myeloma survival with agents such as lenalidomide and daratumumab moving to frontline therapy. Therefore, relapse therapy requires rational sequencing strategies to prioritize effective regimens with each treatment line without compromising access to subsequent lines. At first relapse, most transplant-eligible patients would have undergone RVd (lenalidomide, bortezomib, dexamethasone) induction with subsequent consolidative high-dose therapy with autologous stem cell rescue and Len (lenalidomide) maintenance. For transplant-ineligible patients, frontline therapy with DRd (daratumumab, lenalidomide, dexamethasone) has become the standard of care until myeloma progression or drug intolerance. With the increasing adoption of quadruple therapy in frontline treatment, a significant proportion of patients will soon be multi-class exposed or refractory at early relapse, including exposure to daratumumab, lenalidomide, and bortezomib. This shift necessitates careful consideration of treatment sequences based on available regimens, which include previous treatment responses, cytogenetic and molecular risk profiles (e.g., high-risk versus standard-risk disease), disease kinetics at relapse, and the potential benefit of therapies with novel mechanisms of action. Achieving and maintaining sustained minimal residual disease (MRD) negativity is also critical, as patients in this category consistently experience better outcomes, regardless of cytogenetic risk or line of 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".