Subcutaneous daratumumab plus carfilzomib and dexamethasone (D-Kd) versus carfilzomib and dexamethasone (Kd) in patients with relapsed/refractory multiple myeloma who received previous daratumumab treatment: LYNX study
Bibliographic record
Abstract
Daratumumab-based regimens demonstrate clinical efficacy in relapsed/refractory multiple myeloma (RRMM). As more patients receive frontline daratumumab-based therapy, evaluation of daratumumab retreatment is needed. In the phase 2 LYNX study (ClinicalTrials.gov Identifier: NCT03871829), 88 patients with RRMM who received 1–3 prior lines of therapy, one of which contained daratumumab, were randomized to receive subcutaneous daratumumab plus carfilzomib/dexamethasone (D-Kd; n = 44) or carfilzomib/dexamethasone (Kd; n = 44). The primary endpoint was the very good partial response or better (≥VGPR) rate. At the interim futility analysis, no significant differences in ≥ VGPR rates were found between treatment groups; therefore, the null hypothesis of no treatment difference was accepted, leading to study termination. At the final analysis, 45.5% of D-Kd patients and 40.9% of Kd patients achieved ≥ VGPR (odds ratio, 1.2 [90% CI, 0.59–2.46]; p = 0.6757). No new safety concerns were identified. Future studies are needed to optimize daratumumab-based regimens for patients with RRMM who have prior daratumumab exposure.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".