Predictors of Outcomes of Therapy-Related Acute Myeloid Leukemia After Allogeneic Hematopoietic Stem Cell Transplantation
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Existing literature on allogeneic hematopoietic stem cell transplantation (allo-HSCT) in therapy-related acute myeloid leukemia (t-AML) is confounded by the inclusion of patients with secondary AML and t-MDS. We aim to report our 20-year experience of HSCT in t-AML. METHODS: We retrospectively reviewed patients with t-AML who underwent HSCT. Patients were analyzed for prior malignancy, therapy, time to diagnosis of t-AML, transplant details, relapse-free survival (RFS), overall survival (OS), and predictors of outcomes. RESULTS: In total, 68 patients (59.9% female; median age, 56.5 years) underwent HSCT. Acute and chronic graft-versus-host disease (GVHD) occurred in 39 (57.4%) and 23 (33.8%) patients, respectively. Cumulative incidence of relapse, nonrelapse mortality, RFS, and OS at 2 years were 17.9%, 34.5%, 47.6%, and 49.3%, respectively. Significant predictors of reduced OS were presence of 11q23 rearrangement (hazard ratio [HR], 3.24), using induction regimens other than FLAGI-da or 7 + 3 (HR, 3.65), haploidentical donors (HR, 3.48), Eastern Cooperative Oncology Group performance status 2 or higher (HR, 5.83), and using cyclosporine A-methotrexate as GVHD prophylaxis (HR, 2.41). A significant decrement in survival was seen with an increasing number of any of these prognostic factors. CONCLUSION: Outcomes of t-AML are satisfactory after allo-HSCT. Patients with t-AML with good-risk karyotypes, good performance status, having HLA-matched donors, and receiving intensive induction regimens have better outcomes after HSCT.
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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".