Rates of peripheral neuropathy (PN) in patients (Pts) with relapsed and refractory multiple myeloma (RRMM) treated with carfilzomib vs comparators in pivotal phase III trials.
Bibliographic record
Abstract
8041 Background: PN is a dose-limiting toxicity for some anti-MM agents, such as the proteasome inhibitor (PI) bortezomib (V). Carfilzomib (K), a novel irreversible PI associated with low PN, was evaluated in 2 recent phase 3 studies in RRMM pts. Methods: This analysis evaluated PN rates in ASPIRE (K [27 mg/m2]-lenalidomide [R]-dexamethasone[d] [KRd] vs Rd in relapsed MM; Stewart 2015) and ENDEAVOR (Kd [K 56 mg/m2] vs Vd in RRMM; Dimopoulos 2016). We evaluated grade ≥2 PN during treatment, patient-reported outcomes (PROs; QLQ-C30 pain, FACT/GOG-neurotoxicity subscales), and progression-free survival (PFS) in pts with BL history of PN. Results: In ASPIRE, grade ≥2 PN rate was low (8.9% [KRd] vs 8.0% [Rd]; Table). Pain subscale scores were similar between arms. Median PFS was longer with KRd vs Rd for pts with BL grade ≥2 PN. In ENDEAVOR, grade ≥2 PN rate during the study (prespecified key secondary endpoint) was significantly lower with Kd vs Vd (6.0% vs 32.0%; Table). Pts had significantly improved pain and neurotoxicity subscale scores with Kd vs Vd. PFS improved with Kd vs Vd in pts with BL history of grade ≥2 PN (Table). Conclusions: In ENDEAVOR, Kd resulted in less PN vs Vd; in ASPIRE, PN rate was similar for KRd vs Rd. PFS was longer with KRd and Kd vs Rd and Vd, respectively, including in pts with BL grade ≥2 PN. Improved pain and neurotoxicity outcomes with K may be attributed to better disease control and/or lower PN rates. Clinical trial information: NCT01568866, NCT01080391. [Table: see text]
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".