Race and Outcomes of Autologous Hematopoietic Cell Transplantation for Multiple Myeloma
Bibliographic record
Abstract
Blacks are twice as likely to develop and die from multiple myeloma (MM), and are less likely to receive an autologous hematopoietic-cell transplant (AHCT) for MM compared to Whites. The influence of race on outcomes of AHCT for MM is not well described. We compared the probability of overall survival (OS), progression-free survival (PFS), disease progression, and nonrelapse mortality (NRM) among Black (N=303) and White (N=1892) recipients of AHCT for MM, who were reported to the Center for International Blood and Marrow Transplant Research (CIBMTR) from 1995 to 2005. The Black cohort was more likely to be female, and had better Karnofsky performance scores, but lower hemoglobin and albumin levels at diagnosis. Black recipients were younger and more likely to be transplanted later in their disease course. Disease stage and treatment characteristics prior to AHCT were similar between the 2 groups. Black and White recipients had similar probabilities of 5-year OS (52% versus 47%, P=.19) and PFS (19% versus 21%, P=.64) as well as cumulative incidences of disease progression (72% versus 72%, P=.97) and NRM (9% versus 8%, P=.52). In multivariate analyses, race was not associated with any of these endpoints. Black recipients of AHCT for MM have similar outcomes compared to Whites, suggesting that the reasons underlying lower rates of AHCT in Blacks need to be studied further to ensure equal access to effective therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".