Outcome of carfilzomib/pomalidomide‐based regimens after daratumumab‐based treatment in relapsed multiple myeloma: A Canadian Myeloma Research Group Database analysis
Bibliographic record
Abstract
INTRODUCTION: Although daratumumab-containing regimens improve multiple myeloma (MM) outcomes, recurrence is inevitable. METHODS AND OBJECTIVE: We performed a retrospective study using the Canadian Myeloma Research Group Database to benchmark the efficacy of carfilzomib- or pomalidomide-based therapies immediately following progression on daratumumab treatment. RESULTS: We identified 178 such patients; median number of prior lines of therapy was 3, 97% triple-class exposed, and 60% triple-class refractory. In our cohort, 75 received a subsequent carfilzomib-based therapy, 79 received a pomalidomide-based therapy, and 24 received a treatment with both immunomodulatory drug (IMiD) and proteasome inhibitor (PI) using carfilzomib and/or pomalidomide. The median progression-free survival (PFS) and overall survival (OS) for the entire cohort were 4.5 and 14.2 months, respectively. Carfilzomib-based therapy yielded a median PFS and OS of 4.5 and 10.2 months, respectively, compared to 5.2 and 21.7 months for pomalidomide-based therapy. Patients who received both IMiD and PI with carfilzomib and/or pomalidomide had a median PFS and OS of 4.1 and 14.5 months, respectively. CONCLUSION: Our observations demonstrate the poor outcome of MM patients when standard regimens based on carfilzomib and/or pomalidomide are utilized directly after daratumumab-based therapy given in the relapsed setting. Novel therapies, including immune therapies, are urgently needed to improve the outcomes of these daratumumab-exposed patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".