Real-World Treatment Patterns and Survival Outcomes of Patients with Relapsed/Refractory Multiple Myeloma Treated with a Selinexor-Containing Triplet-Based Regimen
Bibliographic record
Abstract
Treatment for relapsed/refractory multiple myeloma (RRMM) is complex, with several classes of drugs that can be combined into doublet, triplet, or quadruplet regimens. Real-world studies can help to determine the optimal treatment sequences and dosing through observed usage of drugs outside of clinical trials. Previous clinical trials have demonstrated high rates of durable responses in the treatment of patients with triple-class-exposed RRMM with regimens containing selinexor, a first-in-class, orally available selective exportin 1 inhibitor. This study analyzed real-world treatment patterns and survival outcomes using a nationwide electronic health record-derived, deidentified database of patients with RRMM treated with an eligible selinexor-containing, triplet-based regimen, including combinations with dexamethasone and pomalidomide, bortezomib, carfilzomib, or daratumumab. Patients had a real-world overall survival (rwOS) of 14.7 months (95% CI: 10.6, 20.9) and a derived progression-free survival (dPFS) of 4.7 months (95% CI: 3.4, 6.7). Patients with previous exposure to anti-CD38 monoclonal antibodies (mAbs) in the most recent regimen prior to the selinexor treatment had numerically higher survival outcomes (rwOS, 20.9; dPFS, 8.7 months). These data suggest that, in the real-world setting, the use of selinexor triplet regimens is effective in patients with RRMM, especially those with prior exposure to an anti-CD38 mAb in the immediate prior line of therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".