Comparison of Twin and Autologous Transplants for Multiple Myeloma
Bibliographic record
Abstract
Relapse is the overwhelming cause of treatment failure after autologous transplantation for multiple myeloma (MM). For patients with a syngeneic donor, twin transplants provide a healthy graft that is free of myeloma. The relative impact of the graft on posttransplant relapse can be estimated by comparing risk of relapse after hematopoietic cell transplantation from genetically identical twins versus autotransplants because confounding differences in minor or major histocompatibility antigens are absent in the syngeneic transplant setting. Outcomes of 43 subjects who received twin transplants for MM were compared to 170 matched autotransplant recipients reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). Multivariate analysis was performed by fitting a Cox model stratified on matched pairs. The matched transplant patients studied were similar with respect to subject-, disease-, and transplant-related characteristics. Cumulative incidence of relapse/progression was significantly lower, and progression-free survival (PFS) was significantly higher following twin transplants. In multivariate analysis, the probability of relapse/progression was lower in twins (relative risk [RR] = 0.49, 95% confidence interval [CI] 0.28-0.86, P = .011). Twin transplants have a significantly lower relapse risk than autotransplants in MM, suggesting that graft composition may impact outcomes following high-dose chemotherapy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".