Matching-adjusted indirect comparison of talquetamab vs selinexor-dexamethasone and vs belantamab mafodotin in patients with relapsed/refractory multiple myeloma
Bibliographic record
Abstract
OBJECTIVE: Talquetamab is the first GPRC5D-targeting bispecific antibody approved for the treatment of triple-class exposed (TCE) relapsed/refractory multiple myeloma (RRMM). This matching-adjusted indirect comparison (MAIC) study was conducted to compare the effectiveness of talquetamab vs selinexor-dexamethasone (sel-dex) and vs belantamab mafodotin (belamaf) in patients with TCE RRMM. METHODS: An unanchored MAIC was performed using individual patient-level data from patients treated with subcutaneous talquetamab 0.4 mg/kg weekly (QW) and 0.8 mg/kg every other week (Q2W) from MonumenTAL-1 (NCT03399799/NCT04636552) and published summary data for sel-dex from STORM (NCT02336815) and belamaf from DREAMM-2 (NCT0325678). Patients from MonumenTAL-1 who met key eligibility criteria for STORM and DREAMM-2 were included. Outcomes of interest were overall response rate (ORR), complete response or better (≥CR), duration of response (DOR), progression-free survival (PFS), and overall survival (OS). RESULTS: After adjustment for cross-trial differences, patients treated with both dosing schedules of talquetamab showed significantly better ORR, ≥CR, and DOR vs sel-dex and significantly higher ORR and ≥ CR vs belamaf; DOR was relatively similar to belamaf. PFS was significantly improved with talquetamab Q2W and numerically in favor of talquetamab QW vs sel-dex and significantly improved with both dosing schedules of talquetamab vs belamaf. OS was significantly improved with both dosing schedules of talquetamab vs sel-dex and was numerically in favor of both dosing schedules of talquetamab vs belamaf. CONCLUSION: These analyses show superior effectiveness of both talquetamab dosing schedules vs sel-dex and vs belamaf for most outcomes and highlight talquetamab as an effective treatment option for patients with TCE 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".