Alemtuzumab with fludarabine and cyclophosphamide reduces chronic graft-versus-host disease after allogeneic stem cell transplantation for acquired aplastic anemia
Bibliographic record
Abstract
We evaluated a novel alemtuzumab-based conditioning regimen in HSCT for acquired severe aplastic anemia (SAA). In a multicenter retrospective study, 50 patients received transplants from matched sibling donors (MSD; n = 21) and unrelated donors (UD; n = 29), using fludarabine 30 mg/m² for 4 days, cyclophosphamide 300 mg/m² for 4 days, and alemtuzumab median total dose of 60 mg (range:40-100 mg). Median age was 35 years (range 8-62). Overall survival at 2 years was 95% ± 5% for MSD and 83% for UD HSCT (p 0.34). Cumulative incidence of graft failure was 9.5% for MSD and 14.5% for UD HSCT. Full-donor chimerism (FDC) in unfractionated peripheral blood was 42%; no patient achieved CD3 FDC. Acute GVHD was observed in only 13.5% patients (all grade I-II) and only 2 patients (4%) developed chronic GVHD. A low incidence of viral infections was seen. Factors influencing overall survival were HSCT comorbidity 2-year index (92% with score 0-1 vs 42% with score ≥ 2, P < .001) and age (92% for age < 50 years vs 71% ≥ 50 years, P < .001). Our data suggest that the use of an alemtuzumab-based HSCT regimen for SAA results in durable engraftment with a low incidence of chronic GVHD.
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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 | 0.000 |
| 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) | 0.000 | 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".