Matching‐adjusted indirect comparison of efficacy and safety of bortezomib, thalidomide, and dexamethasone (VTd) as per label compared with modified VTd dosing schedules in patients with newly diagnosed multiple myeloma who are transplant eligible
Bibliographic record
Abstract
Background: The combination of bortezomib, thalidomide, and dexamethasone (VTd) is a standard of care for transplant-eligible patients with newly diagnosed multiple myeloma (NDMM). Although approved labeling for VTd includes an escalating thalidomide dose up to 200 mg daily (VTd-label), a lower fixed dose of thalidomide (100 mg daily; VTd-mod) has become commonplace in clinical practice. To date, no clinical trials comparing VTd-mod with VTd-label have been performed. Here, we compared outcomes for VTd-mod with VTd-label using a matching-adjusted indirect comparison. Methods: VTd-mod data were from NCT02541383 (CASSIOPEIA; phase III) and NCT00531453 (phase II); VTd-label data were from NCT00461747 (PETHEMA/GEM; phase III). To adjust for heterogeneity, baseline characteristics from VTd-label were weighted to match VTd-mod. Outcomes included overall survival (OS), progression-free survival (PFS), postinduction and posttransplant responses, and safety. Results: VTd-mod was noninferior to VTd-label for OS, postinduction overall response rate (ORR), and very good partial response or better (≥VGPR). VTd-mod was significantly better than VTd-label for PFS, posttransplant ORR, and ≥VGPR. VTd-mod was noninferior to VTd-label for safety outcomes, and inferior to VTd-label for postinduction and posttransplant complete response or better. Conclusions: Our analysis supports the continued use of VTd-mod in clinical practice in transplant-eligible NDMM patients.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".