Global characteristics and outcomes of autologous hematopoietic stem cell transplantation for newly diagnosed multiple myeloma: A study of the worldwide network for blood and marrow transplantation (<scp>WBMT</scp>)
Bibliographic record
Abstract
Autologous hematopoietic cell transplantation (AHCT) is a commonly used treatment in multiple myeloma (MM). However, real-world global demographic and outcome data are scarce. We collected data on baseline characteristics and outcomes from 61 725 patients with newly diagnosed MM who underwent upfront AHCT between 2013 and 2017 from nine national/international registries. The primary endpoint was overall survival (OS), and the secondary endpoints were progression-free survival (PFS), relapse incidence (RI) and non-relapse mortality (NRM). Median OS amounted to 90.2 months (95% CI 88.2-93.6) and median PFS 36.5 months (95% CI 36.1-37.0). At 24 months, cumulative RI was 33% (95% CI 32.5%-33.4%) and NRM was 2.5% (95% CI 2.3%-2.6%). In the multivariate analysis, superior outcomes were associated with younger age, IgG subtype, complete hematological response at auto-HCT, Karnofsky score of 100%, international staging scoring (ISS) stage 1, HCT-comorbidity index (CI) 0, standard cytogenetic risk, auto-HCT in recent years, and use of lenalidomide maintenance. There were differences in the baseline characteristics and outcomes between registries. While the NRM was 1%-3% at 12 months worldwide, the OS at 36 months was 69%-84%, RI at 12 months was 12%-24% and PFS at 36 months was 43%-63%. The variability in these outcomes is attributable to differences in patient and disease characteristics as well as the use of maintenance and macroeconomic factors. In conclusion, worldwide data indicate that AHCT in MM is a safe and effective therapy with an NRM of 1%-3% with considerable regional differences in OS, PFS, RI, and patient characteristics. Maintenance treatment post-AHCT had a beneficial effect on OS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".