Outcomes of daratumumab in the treatment of multiple myeloma: A retrospective cohort study from the Canadian Myeloma Research Group Database
Bibliographic record
Abstract
Daratumumab (dara) has significantly altered the therapeutic landscape of multiple myeloma (MM), especially in the relapsed setting. This study aimed to evaluate the outcomes of dara-containing regimens in the Canadian real-world setting among relapsed and refractory MM available within the national Canadian Myeloma Research Group Database (CMRG-DB). A total of 583 MM patients who received dara-based therapy in second-line or later treatment were included. After a median follow-up of 17.5 months, the median progression-free survival (PFS) and overall survival (OS) for the entire cohort were 13.1 and 32.9 months, respectively. The median PFS and OS were 23.5 and 49.1 months in second-line treatment and decreased to 12.8 and 43.0 months in third-line and 7.0 and 20.5 months in fourth-line treatment respectively. Dara in monotherapy with or without corticosteroids after a median of four prior lines of therapy resulted in a median PFS of 3.9 months and a median OS of 17.1 months. The addition of bortezomib, lenalidomide or pomalidomide to dara resulted in an improved median PFS and OS of 8.3 and 26.2 months; 26.8 and 43.0 months; and 9.7 and 31.4 months respectively. These retrospective data from the CMRG-DB suggest that outcomes are superior when dara is used in combination and in earlier lines of treatment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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".