Unrelated Donor Allogeneic Transplantation after Failure of Autologous Transplantation for Acute Myelogenous Leukemia: A Study from the Center for International Blood and Marrow Transplantation Research
Bibliographic record
Abstract
The survival of patients with relapsed acute myelogenous leukemia (AML) after autologous hematopoietic stem cell transplantation (auto-HCT) is very poor. We studied the outcomes of 302 patients who underwent secondary allogeneic hematopoietic cell transplantation (allo-HCT) from an unrelated donor (URD) using either myeloablative (n = 242) or reduced-intensity conditioning (RIC; n = 60) regimens reported to the Center for International Blood and Marrow Transplantation Research. After a median follow-up of 58 months (range, 2 to 160 months), the probability of treatment-related mortality was 44% (95% confidence interval [CI], 38%-50%) at 1-year. The 5-year incidence of relapse was 32% (95% CI, 27%-38%), and that of overall survival was 22% (95% CI, 18%-27%). Multivariate analysis revealed a significantly better overal survival with RIC regimens (hazard ratio [HR], 0.51; 95% CI, 0.35-0.75; P <.001), with Karnofsky Performance Status score ≥90% (HR, 0.62; 95% CI, 0.47-0.82: P = .001) and in cytomegalovirus-negative recipients (HR, 0.64; 95% CI, 0.44-0.94; P = .022). A longer interval (>18 months) from auto-HCT to URD allo-HCT was associated with significantly lower riak of relapse (HR, 0.19; 95% CI, 0.09-0.38; P <.001) and improved leukemia-free survival (HR, 0.53; 95% CI, 0.34-0.84; P = .006). URD allo-HCT after auto-HCT relapse resulted in 20% long-term leukemia-free survival, with the best results seen in patients with a longer interval to secondary URD transplantation, with a Karnofsky Performance Status score ≥90%, in complete remission, and using an RIC regimen. Further efforts to reduce treatment-related mortaility and relapse are still needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".