The Global State of Hematopoietic Cell Transplantation for Multiple Myeloma: An Analysis of the Worldwide Network of Blood and Marrow Transplantation Database and the Global Burden of Disease Study
Bibliographic record
Abstract
Multiple myeloma (MM) is a plasma cell neoplasm characterized by destructive bony lesions, anemia, and renal impairment. Access to effective therapy is limited globally. We report the rates and utilization of hematopoietic cell transplantation (HCT) globally from 2006-2015 to better characterize access to HCT for patients with MM. This was an analysis of a retrospective survey of Worldwide Network of Blood and Marrow Transplant sites, conducted annually between 2006-2015. Incidence estimates were from the Global Burden of Disease study. Outcome measures included total number of autologous and allogeneic HCTs by world regions, and percentage of newly diagnosed MM patients who underwent HCT, calculated by the number of transplants per region in calendar year/gross annual incidence of MM per region. From 2006 to 2015, the number of autologous HCT performed worldwide for MM increased by 107%. Utilization of autologous HCT was highest in Northern America and European regions, increasing from 13% to 24% in Northern America, and an increase from 15% to 22% in Europe. In contrast, the utilization of autologous HCT was lower in the Africa/Mediterranean region, with utilization only changing from 1.8% in 2006 to 4% in 2015. The number of first allogeneic HCT performed globally for MM declined after a peak in 2012 by -3% since 2006. Autologous HCT utilization for MM has increased worldwide in high-income regions but remains poorly utilized in Africa and the East Mediterranean. More work is needed to improve access to HCT for MM patients, especially in low to middle income countries. © 2020 American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".