Recent progress in relapsed multiple myeloma therapy: implications for treatment decisions
Bibliographic record
Abstract
The availability of novel therapies for the treatment of multiple myeloma has had a dramatic impact on the depth of response that can be expected on initial treatment. Despite these advances, disease relapse remains inevitable in most patients and brings with it a different set of priorities for therapy. The most recent wave of novel agents may have a particular impact in the relapsed setting. In this review, we examine the evidence currently available from clinical trials for the use of novel agents, particularly in the formation of triplet therapy. We consider data supporting the addition of the proteasome inhibitors carfilzomib and ixazomib, or the monoclonal antibodies elotuzumab or daratumumab, to a treatment backbone of lenalidomide and dexamethasone. The clinical data set is less well developed for the addition of a third agent to the combination of bortezomib and dexamethasone; nonetheless, data are presented supporting the addition of the histone deacetylase inhibitor panobinostat, or elotuzumab or daratumumab. While acknowledging the lack of head-to-head data on which to base comparisons between the numerous regimens, we collate the latest data in order to provide a basis on which to make clinical decisions in this rapidly advancing field.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".