Once Daily i.v. Busulfan and Fludarabine (i.v. Bu-Flu) Compares Favorably with i.v. Busulfan and Cyclophosphamide (i.v. BuCy2) as Pretransplant Conditioning Therapy in AML/MDS
Bibliographic record
Abstract
We postulated that fludarabine (Flu) instead of cyclophosphamide (Cy) combined with i.v. busulfan (Bu) as preconditioning for allogeneic hematopoietic stem cell transplantation (HSCT) would improve safety and retain antileukemic efficacy. Sixty-seven patients received BuCy2, and subsequently, 148 patients received Bu-Flu. We used a Bayesian method to compare outcomes between these nonrandomized patients. The groups had comparable pretreatment characteristics, except that Bu-Flu patients were older (46 versus 39 years, P < .01), more often had unrelated donors (47.3% versus 20.9%, P < .0003), and had shorter median follow-up (39.7 versus 74.6 months). To account for improved supportive care and other unidentified factors that may affect outcome ("period" effects), 78 acute myelogenous leukemia (AML) patients receiving Melphalan-Flu (MF), treated in parallel during this time (1997-2004) were used to estimate the period effect. The MF patients' outcomes worsened during this period. Therefore, the period effect is unlikely to explain the greatly improved outcome with Bu-Flu. Patients transplanted with Bu-Flu in the first complete remission (CR1) had a 3-year overall survival and event-free-survival (EFS) of 78% and 74%, respectively, whereas CR1 patients younger than age 41 had a 3-year EFS of 83%. These results support replacing BuCy +/- ATG with Bu-Flu +/- rabbit-antithymocyte globulin (ATG), and warrant a prospective comparison between allogeneic HSCT and conventional induction/consolidation chemotherapy for AML in CR1.
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) | 0.000 | 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".