Impact of elranatamab on quality of life: Patient‐reported outcomes from <scp>MagnetisMM</scp>‐3
Bibliographic record
Abstract
The physical and emotional burden of relapsed or refractory multiple myeloma (RRMM) has been strongly correlated with declining health-related quality of life (QOL) in the patients it affects. This analysis evaluated patient-reported outcomes (PROs) from B-cell maturation antigen (BCMA)-naive (n = 123) and -exposed (n = 64) patients with RRMM enrolled in the MagnetisMM-3 study (NCT04649359) and treated with the humanized, bispecific BCMA-CD3 antibody elranatamab. Patients received two step-up doses of elranatamab (12 mg on day 1, 32 mg on day 4) before starting the full dose of 76 mg on day 8 (each cycle = 28 days). Global health status, functioning and symptom data were collected electronically using validated and myeloma-specific questionnaires. Improvements in PROs occurred early, with marked reductions in pain and disease symptoms and notable improvements in patients' outlook for their future health. Additionally, 40.2% of BCMA-naive and 52.6% of BCMA-exposed patients perceived their disease as 'a little better' or 'much better' by Cycle 1, Day 15. The results from this analysis demonstrated that elranatamab maintained or improved symptomology and general health status, regardless of prior BCMA-directed therapy. Thus, in addition to its clinical benefits, elranatamab therapy may sustain or improve QOL in heavily pretreated patients with RRMM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".