Daratumumab/lenalidomide/dexamethasone in transplant-ineligible newly diagnosed myeloma: MAIA long-term outcomes
Bibliographic record
Abstract
In the MAIA study, daratumumab plus lenalidomide and dexamethasone (D-Rd) improved progression-free survival (PFS) and overall survival (OS) versus lenalidomide and dexamethasone (Rd) alone in transplant-ineligible patients with newly diagnosed multiple myeloma (NDMM). We report updated efficacy and safety from MAIA (median follow-up, 64.5 months), including a subgroup analysis by patient age (<70, ≥70 to <75, ≥75, and ≥80 years). Overall, 737 transplant-ineligible patients with NDMM were randomized 1:1 to D-Rd or Rd. The primary endpoint, PFS, was improved with D-Rd versus Rd (median, 61.9 vs 34.4 months; hazard ratio [HR], 0.55; 95% confidence interval [CI], 0.45-0.67; P < 0.0001). Median OS was not reached in the D-Rd group versus 65.5 months in the Rd group (HR, 0.66; 95% CI, 0.53-0.83; P = 0.0003); estimated 60-month OS rates were 66.6% and 53.6%, respectively. D-Rd achieved higher rates of complete response or better (≥CR; 51.1% vs 30.1%), minimal residual disease (MRD) negativity (32.1% vs 11.1%), and sustained MRD negativity (≥18 months: 16.8% vs 3.3%) versus Rd (all P < 0.0001). D-Rd demonstrated clinically meaningful efficacy benefits across age groups. No new safety concerns were observed. Updated results (median follow-up, >5 years) continue to support frontline use of D-Rd in transplant-ineligible patients with NDMM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".