Allogeneic stem cell transplant in myelodysplastic syndrome‐factors impacting survival
Bibliographic record
Abstract
OBJECTIVES: The primary aim of this study was to evaluate survival outcomes following allo-HCT in myelodysplastic syndrome (MDS), and the secondary aim was to study variables impacting survival. METHODS: This analysis describes patient characteristics, treatment, and outcomes in 125 consecutive adult patients with MDS transplanted from 2005 to 2018. RESULTS: The median age was 61 years, and median follow-up in patients alive at last follow-up was 29 months. The 2-year OS and RFS were 39% (95%CI 30%-48%) and 35.3% (95% CI: 27%-44%), respectively. Transfusion dependence, high-risk cytogenetics, and high serum ferritin were independent risk factors for death. The cumulative incidence of relapse (CIR) and non-relapse mortality (NRM) at 2 years were 23% and 41.6%, respectively. High serum ferritin was significantly associated with NRM. There was no association between the percentage of bone marrow blasts (either at diagnosis or at pretransplant evaluation), on relapse or survival. Induction chemotherapy did not offer any survival advantage in MDS RAEB-2 patients compared to cytoreduction with azacytidine alone. CONCLUSION: Our results highlight the importance of karyotype on survival after allo-HCT and identify serum ferritin and transfusion dependence as important surrogate markers of outcome. In addition, our results demonstrate the efficacy of azacytidine for pretransplant cytoreduction.
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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 | 0.000 | 0.001 |
| 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".