Post‐transplant consolidation plus lenalidomide maintenance vs lenalidomide maintenance alone in multiple myeloma: A systematic review
Bibliographic record
Abstract
BACKGROUND: In newly diagnosed multiple myeloma (NDMM), autologous stem cell transplantation (ASCT) remains the standard approach for transplant-eligible patients. To control the inevitable relapse, post-transplant consolidation/maintenance strategies are commonly used. However, the benefit of post-transplant consolidation is still uncertain METHOD: We conducted a systematic review of phase II/III studies to compare the efficacy of post-ASCT consolidation plus lenalidomide maintenance (CON+LEN) vs lenalidomide maintenance alone (LEN alone) in NDMM. A meta-analysis using fixed and random effects models was performed. RESULTS: Fourteen studies were included with 2275 participants with NDMM treated with ASCT and lenalidomide maintenance. Two groups were identified: CON+LEN group (n = 1102) and LEN alone group (n = 1173). There was no statistically significant difference in the complete response rate between the two groups [RR = 1.1; 95% CI: 0.83-1.44; P = .490]. Interestingly, we found that very good partial response or better rate is around 1.5-fold significantly higher in the CON+LEN group compared to LEN alone group [RR: 1.46; 95% CI: 1.25-1.70; P < .0001]. However, there was no significant difference between the two groups regarding PFS [RR: 1.0; 95% CI: 0.92-1.08, P = .929] and OS [RR: 0.9; 95% CI: 0.92-1.01; P = .148] at 3-4 years follow-up. The risk of secondary primary malignancy (SPM) was also similar between the two groups (RR: 1.2; 95% CI: 0.84-1.92; P = .2). Data on adverse events were limited. CONCLUSION: Our data suggest that, in NDMM patients treated with upfront ASCT, post-transplant consolidation may improve depth of response, but does not add to OS or PFS, compared to lenalidomide maintenance alone. However, data in this context are still immature.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".