Gaps and opportunities in the treatment of relapsed-refractory multiple myeloma: Consensus recommendations of the NCI Multiple Myeloma Steering Committee
Bibliographic record
Abstract
A wide variety of new therapeutic options for Multiple Myeloma (MM) have recently become available, extending progression-free and overall survival for patients in meaningful ways. However, these treatments are not curative, and patients eventually relapse, necessitating decisions on the appropriate choice of treatment(s) for the next phase of the disease. Additionally, an important subset of MM patients will prove to be refractory to the majority of the available treatments, requiring selection of effective therapies from the remaining options. Immunomodulatory agents (IMiDs), proteasome inhibitors, monoclonal antibodies, and alkylating agents are the major classes of MM therapies, with several options in each class. Patients who are refractory to one agent in a class may be responsive to a related compound or to a drug from a different class. However, rules for selection of alternative treatments in these situations are somewhat empirical and later phase clinical trials to inform those choices are ongoing. To address these issues the NCI Multiple Myeloma Steering Committee formed a relapsed/refractory working group to review optimal treatment choices, timing, and sequencing and provide recommendations. Additional issues considered include the role of salvage autologous stem cell transplantation, risk stratification, targeted approaches for genetic subsets of MM, appropriate clinical trial endpoints, and promising investigational agents. This report summarizes the deliberations of the working group and suggests potential avenues of research to improve the precision, timing, and durability of treatments for Myeloma.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".