Interaction Between High-Dose Intravenous Busulfan and Post-Transplantation Cyclophosphamide on Hemorrhagic Cystitis After Allogeneic Hematopoietic Cell Transplantation
Bibliographic record
Abstract
This study investigates the incidence and predictors of hemorrhagic cystitis (HC) in 960 adults undergoing allo- hematopoietic stem cell transplantation. Two hundred fifty-two (26.5%) patients received myeloablative conditioning regimens, and 81.4% received high-dose intravenous busulfan (HD Bu). Six hundred ninety-five (72.4%) patients received post-transplantation cyclophosphamide (PTCY)-based prophylaxis, and 91.4% additionally received anti-thymocyte globulin (ATG) and Cyclosporine A (CsA) (PTCY-ATG-CsA). Two hundred twenty-eight (23.8%) patients developed HC. The day 100 cumulative incidences of grades 2-4 and 3-4 HC were 11.1% and 4.9%. BK virus was isolated in 58.3% of urinary samples. Using HD BU myeloablative regimens increased the risk for grade 2-4 HC (hazard ratio [HR] = 1.97, P = .035), and HD BU combined with ATG-PTCY-CsA increased this 4 times (HR = 4.06, P < .001) for grade 2-4 HC compared to patients who received neither of these drugs. A significant correlation was documented between grade II-IV acute graft-versus-host disease and grade 2-4 HC (HR = 2.10, P < .001). Moreover, patients with BK-POS grade 2-4 HC had lower 1-year overall survival (HR = 1.51, P = .009) and higher non-relapse mortality (HR = 2.31, P < .001), and patients with BK-NEG grade 2-4 HC had comparable post-transplantation outcomes. In conclusion, intravenous HD Bu was identified as a predictor for grade 2-4 HC. Moreover, when HD Bu was combined with PTCY-ATG-CsA, the risk increased 4-fold. Based on the results provided by this study, preventing the onset of HC, especially in high-risk patients, is mandatory because its presence significantly increases the risk for mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".