Daratumumab combined with dexamethasone and lenalidomide or bortezomib in relapsed/refractory multiple myeloma (RRMM) patients: Report from the multiple myeloma GIMEMA Lazio group
Bibliographic record
Abstract
The multiple myeloma (MM) treatment has changed over the last years due to the introduction of novel drugs. Despite improvements in the MM outcome, MM remains an incurable disease. Daratumumab is a human IgGK monoclonal antibody targeting CD38 with tumor activity associated with immunomodulatory mechanism. In combination with standard of care regimens, including bortezomib (Vd) or lenalidomide (Rd), daratumumab prolonged progression-free survival (PFS) in patients (pts) with relapsed/refractory multiple myeloma (RRMM) and in new diagnosis MM. We report the data of the MM GIMEMA Lazio group in 171 heavily treated pts who received daratumumab, lenalidomide and dexamethasone (DRd) or daratumumab, velcade and dexamethasone (DVd). The overall response rate was 80%, and the overall survival (OS) and PFS were 84% and 77%, respectively. In addition, pts treated with DRd showed a better median PFS compared to pts treated with DVd, at 12 and 24 months, respectively. The most common hematologic treatment-emergent adverse events (TAEs) were neutropenia, thrombocytopenia, and anemia. The most common nonhematologic TAEs were peripheral sensory neuropathy and infections. Our data confirmed that DRd or DVd therapy is effective and safe in RRMM pts, and our real-life analysis could support the physicians regarding the choice of optimal therapy in this setting of pts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".