Effect of Disease Stage and Time Since Diagnosis on Time to Progression for Pegylated Liposomal Doxorubicin + Bortezomib vs Bortezomib Alone in Relapsed or Refractory Multiple Myeloma.
Bibliographic record
Abstract
Abstract A recent report of a phase III randomized trial with pegylated liposomal doxorubicin (PLD)+bortezomib vs. bortezomib alone in relapsed or refractory multiple myeloma (RRMM) demonstrated improved time to progression (TTP) with PLD+bortezomib (Orlowski, JCO 2007). The present post-hoc analyses evaluated the efficacy of PLD+bortezomib according to International Staging System (ISS) disease stage, and time since initial myeloma diagnosis (TSD). Patients with ≥1 prior therapy were randomized to receive PLD at 30 mg/m2 on day 4 and bortezomib at 1.3 mg/m2 on days 1, 4, 8, and 11, or bortezomib alone for up to eight 21-day cycles, or at least 2 cycles beyond CR, or optimal response unless disease progression or unacceptable toxicities occurred. The improved TTP reported previously with PLD+bortezomib over bortezomib alone in the total study population was also observed in the higher risk groups based on disease stage (ISS 2 & 3; Table). TTP was also significantly longer with PLD+bortezomib vs. bortezomib for TSD >2 yrs despite more protracted disease. The therapeutic advantage of prolonged TTP with the PLD+bortezomib combination was maintained consistently across subgroups (heterogeneity tests: ISS 1 vs. 2 vs. 3, p=0.407; TSD ≤2 yrs. vs. >2 yrs., p=0.946). Incidence of grade 3/4 neuropathies was low (<10%) in the two treatment arms in all subgroups. PLD-related hand-foot syndrome was also <10% in all PLD+bortezomib subgroups. Despite high-grade disease or protracted disease history, the PLD+bortezomib combination shows significantly improved TTP as compared to bortezomib alone. Also, the combination therapy was well-tolerated.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".