Non-Myeloablative Allogeneic Hematopoietic Transplantation for Patients with Hematologic Malignancies: 9-Year Single-Centre Experience
Bibliographic record
Abstract
Matched related and unrelated allogeneic nonmyeloablative hematopoietic transplantation (nmt) is increasingly being used in patients with hematologic malignancies. Conditioning regimens and indications for nmt vary considerably from centre to centre. Our institution uses intravenous fludarabine and cyclophosphamide, plus graft-versus-host disease (gvhd) prophylaxis with tacrolimus and mycophenolate mofetil. We retrospectively analyzed 89 consecutive patients who underwent nmt (65 related, 24 unrelated) at our institution from October 2002 to September 2011. The most frequent indications for nmt were acute myelocytic leukemia (high-risk in first complete or subsequent remission: n = 20, 22.5%) and relapsed follicular lymphoma (n = 18, 20.2%). The cumulative incidence of acute gvhd (grades 2-4) was 28.1% (n = 25), and rates were similar for related (n = 18, 28%) and unrelated (n = 7, 29%) nmt. At a median follow-up of 22.6 months, the cumulative incidence of chronic gvhd (limited and extensive) was 68% (n = 61): 68.5% (n = 44) for related and 71% (n = 17) for unrelated nmt. The 100-day transplant-related mortality rate was 2.2%: 1.5% for related and 4.2% for unrelated nmt. Of the 89 patients, 30 (33.7%) have relapsed: 41.5% after related and 12.5% after unrelated nmt. Relapse rates were similar in patients with myeloid and lymphoid malignancies (36.4% vs. 33.3%). The 3-year overall and progression-free survival rates were 50.0% and 43.4% respectively, with multivariate analysis showing that neither rate was affected by age, disease group, status at transplantation, or related compared with unrelated nmt. Our findings indicate that, despite its limitations, including the incidence of chronic gvhd, nmt is an important treatment modality for a selected subgroup of patients with hematologic malignancies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".