Reduced intensity allogeneic stem cell transplant with anti‐thymocyte globulin and post‐transplant cyclophosphamide in acute myeloid leukemia
Bibliographic record
Abstract
OBJECTIVES: We aimed to study the efficacy of reduced intensity conditioning (RIC) allo-HSCT combined with anti-thymocyte globulin (ATG) and post-transplant cyclophosphamide (PTCy) for graft-versus-host disease (GVHD) prophylaxis in AML. METHODS: One hundred forty-seven patients were included. All patients underwent unmanipulated peripheral blood stem cell RIC allo-HSCT. Median follow-up was 12.8 months (range 0.5-39). RESULTS: Median age was 58 years. Twenty-nine (20%) recipients received 10/10 MRD grafts, 69 (47%) 10/10 MUD grafts, 20 (13.6%) 9/10 MMUD, and 29 (20%) haploidentical grafts. The cumulative incidence of grade II-IV and III-IV acute GVHD at day +100, and moderate/severe chronic GVHD at 1-year were as follow: 14.3%, 1.4%, and 8.3%. There were no significant differences according to donor type (P = .46) and cumulative incidence of GVHD. One-year overall survival (OS), relapse-free survival (RFS), non-relapse mortality, and GVHD-free/Relapse-free survival were as follows: 66.9% (95% CI 58.4-74), 59.9%, and 18.7% and 53.7%. KPS ≤ 80 was predictive of worst OS (P = .04). Those recipients who received MUD transplants had better RFS (P = .01). CONCLUSIONS: RIC allo-HSCT combined with ATG and PTCy is safe and a potentially curative strategy and it is associated with impressive GRFS in AML.
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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 | 0.001 |
| 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".