Comparison of Time to Next Treatment or Death Between Front‐Line Daratumumab, Lenalidomide, and Dexamethasone ( <scp>DRd</scp> ) Versus Bortezomib, Lenalidomide, and Dexamethasone ( <scp>VRd</scp> ) Among Transplant‐Ineligible Patients With Multiple Myeloma
Bibliographic record
Abstract
INTRODUCTION: Daratumumab, lenalidomide, and dexamethasone (DRd) and bortezomib, lenalidomide, and dexamethasone (VRd) are the only preferred treatment regimens for patients with transplant-ineligible (TIE) newly diagnosed multiple myeloma (NDMM). As there are no randomized head-to-head studies of DRd versus VRd, this analysis aimed to compare real-world time-to-next-treatment (TTNT) or death in this population. METHODS: Patients with NDMM who received front-line (FL) DRd or VRd were identified from the Acentrus database (January 1, 2018 to May 31, 2023). Those with a record of a stem cell transplant or aged < 65 years were excluded to limit analysis to the TIE population. Inverse probability of treatment weighting was used to balance baseline patient characteristics. A doubly robust Cox proportional hazards model was used to compare TTNT or death between cohorts. RESULTS: = 341), cohorts had similar baseline characteristics. Of these, 98 (32.4%) DRd and 175 (51.2%) VRd patients either received a subsequent line of therapy or died, with a median TTNT or death of 37.8 months in the DRd cohort and 18.7 months in the VRd cohort (hazard ratio: 0.58, 95% confidence interval: 0.35, 0.81; p < 0.001). CONCLUSION: Treatment of TIE NDMM patients with DRd led to a significantly longer TTNT or death compared to VRd, evidenced by a 42% risk reduction, supporting the effectiveness of DRd over VRd as FL treatment in this patient population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".