Low-Dose Total Body Irradiation and Fludarabine Conditioning for HLA Class I-Mismatched Donor Stem Cell Transplantation and Immunologic Recovery in Patients with Hematologic Malignancies: A Multicenter Trial
Bibliographic record
Abstract
HLA-mismatched grafts are a viable alternative source for patients without HLA-matched donors receiving ablative hematopoietic cell transplantation (HCT), although their use in reduced intensity conditioning (RIC) or nonmyeloablative (NMA) conditioning HCT has been not well established. Here, we extended HCT to recipients of HLA class I-mismatched grafts to investigate whether NMA conditioning can establish stable donor engraftment. Fifty-nine patients were conditioned with fludarabine (Flu) 90 mg/m(2) and 2 Gy total body irradiation (TBI), followed by immunosuppression with cyclosporine (CsA) 5.0 mg/kg twice a day and mycophenolate mofetil (MMF) 15 mg/kg 3 times a day for transplantation of granulocyte colony-stimulating factor (G-CSF)-mobilized peripheral blood stem cells (PBSCs) from related (n = 5) or unrelated donors (n = 54) with 1 antigen +/- 1 allele HLA class I mismatch or 2 HLA class I allele mismatches. Sustained donor engraftment was observed in 95% of the evaluable patients. The incidence of grade II-IV acute and extensive chronic graft-versus-host disease (aGVHD, cGVHD) was 69% and 41%, respectively. The cumulative probability of nonrelapse mortality (NRM) was 47% at 2 years. Two-year overall and progression-free survival (OS, PFS) was 29% and 28%, respectively. NMA conditioning with Flu and low-dose TBI, followed by HCT using HLA class I-mismatched donors leads to successful engraftment and long-term survival; however, the high incidence of aGVHD and NRM needs to be addressed by alternate GVHD prophylaxis regimens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".