Improved survival in steroid‐refractory acute graft <i>versus</i> host disease after non‐myeloablative allogeneic transplantation using a daclizumab‐based strategy with comprehensive infection prophylaxis
Bibliographic record
Abstract
Approximately 15% of patients undergoing non-myeloablative allogeneic haematopoietical cell transplantation (NMHCT) develop steroid-refractory acute-graft versus host disease (aGVHD), a usually fatal complication. We encountered 18 cases of steroid-refractory aGVHD in 146 patients, undergoing NMHCT from a related human leucocyte antigen-compatible donor following cyclophosphamide/fludarabine-based conditioning. Our initial cohort of steroid-refractory aGVHD patients treated with antithymocyte globulin (ATG) and mycophenolate mofetil (regimen-1: n = 6) had high GVHD-related mortality. Therefore, we investigated an alternative strategy for subsequent patients developing this complication (regimen-2: n = 12), consisting of daclizumab (alone or combined with infliximab/ATG) and targeted broad spectrum antibacterial and aspergillus prophylaxis in conjunction with rapid tapering of steroids to minimize opportunistic infections. In a retrospective analysis, patients receiving regimen-2 were significantly more likely to have complete resolution of GVHD compared with those receiving regimen-1 [12/12 (100%) vs. 1/6 (17%); P < 0.001]. When compared with those receiving regimen-1, regimen-2 patients also had a higher probability of survival at day 100 (100% vs. 50%) and day 200 (73% vs. 17%) post-transplant, and improved overall survival (median 453 d vs. 42 d from aGVHD onset; P < 0.0001). GVHD-related mortality was 89% for regimen-1 patients vs. 17% for regimen-2 patients (P < 0.0001). These data suggest that a co-ordinated approach using immunoregulatory monoclonal antibodies, pre-emptive antimicrobial therapy and judicious steroid withdrawal can dramatically improve outcome in steroid-refractory aGVHD.
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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) | 0.000 | 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".