Pomalidomide, bortezomib, and dexamethasone at first relapse in lenalidomide‐pretreated myeloma: A subanalysis of OPTIMISMM by clinical characteristics
Bibliographic record
Abstract
OBJECTIVE: We evaluated the efficacy and safety of pomalidomide, bortezomib, and dexamethasone (PVd) vs bortezomib and dexamethasone (Vd) by age, renal function, and high-risk cytogenetic abnormalities in lenalidomide-pretreated patients with multiple myeloma at first relapse. METHODS: OPTIMISMM was a phase 3, multicenter, open-label, randomized study (NCT01734928; N = 559). The primary endpoint was progression-free survival (PFS). RESULTS: Overall, 226 patients had received one prior line of therapy. PVd significantly prolonged PFS vs Vd in patients aged ≤65 years (median, 22.0 vs 13.1 months; P = .0258) and >65 years (median, 17.6 vs 9.9 months; P = .0369). Median PFS in patients with renal impairment (RI; creatinine clearance <60 mL/min) was 15.1 months with PVd vs 9.5 months with Vd (hazard ratio [HR], 0.67 [95% CI, 0.34-1.34]). In patients without RI, median PFS was 22.0 vs 13.1 months (HR, 0.45 [95% CI, 0.27-0.76]). In patients with high-risk cytogenetics, median PFS was 14.7 vs 9.9 months (HR, 0.39 [95% CI, 0.13-1.17]). PVd significantly improved overall response rate vs Vd in all subgroups. The safety profile of PVd was consistent with previous reports. CONCLUSIONS: These findings confirmed the benefits of PVd at first relapse, including in patients with poor prognostic factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".