Comparable Outcomes in Nonsecretory and Secretory Multiple Myeloma after Autologous Stem Cell Transplantation
Bibliographic record
Abstract
Nonsecretory myeloma (NSM) accounts for <5% of cases of multiple myeloma (MM). The outcome of these patients following autologous stem cell transplantation (ASCT) has not been evaluated in clinical trials. We compared the outcomes after ASCT for patients with NSM reported to the Center for International Blood and Marrow Transplant Research (CIBMTR) between 1989 and 2003, to a matched group of 438 patients (4 controls for each patient) with secretory myeloma (SM). The patients were matched using propensity scores calculated using age, Durie-Salmon stage, sensitivity to pretransplant therapy, time from diagnosis to transplant, and year of transplant. Disease characteristics were similar in both groups at diagnosis and at transplant except higher risk of anemia, hypoalbuminemia, and marrow plasmacytosis (in SM) and plasmacytoma (more in NSM). Cumulative incidence of treatment-related mortality (TRM), relapse, progression-free survival (PFS), and overall survival (OS) were similar between the groups. In multivariate analysis, based on a Cox model stratified on matched pairs and adjusted for covariates not considered in the propensity score, we found no difference in outcome between the NSM and SM groups. In this large cohort of patients undergoing ASCT, we found no difference in outcomes of patients with NSM compared to those with SM.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".