Real-World Impact of Routine Addition of Antithymocyte Globulin to Standard GVHD Prophylaxis in Myeloablative Unrelated Donor Transplants: Important Gains in Graft-versus-Host Disease Prevention though No Difference in Overall Survival
Bibliographic record
Abstract
INTRODUCTION: Antithymocyte globulin (ATG) has been demonstrated to reduce the incidence of graft-versus-host disease (GVHD); however, it remains controversial whether these gains are offset by an increase in relapse. METHODS: We conducted a retrospective historical control study consisting of patients (n = 210) who underwent myeloablative allogeneic hematopoietic stem-cell transplantation (HSCT) from 2014 to 2020. RESULTS: The incidence of acute GVHD was lower in the ATG group (51.4%) than the non-ATG group (control) (70.0%, p = 0.010). The incidence of chronic GVHD was also lower in the ATG group at 1-year (36.4% vs. 62.9%, p < 0.001) and 2-year (40.0% vs. 65.7%, p < 0.001) post-HSCT. The mortality due to GVHD was higher in the control (18.5%) than the ATG group (4.3%; p = 0.024). The severe GVHD-relapse-free survival was higher in the ATG group (36.4%) than the control (12.9%; p < 0.001). Nevertheless, the 2-year overall survival was similar. CONCLUSION: Our results confirm the effectiveness of ATG in prevention of GVHD in the real-world setting and enhanced GVHD-free survival. An important result is the equalization of overall survival between the ATG and control groups at 1- and 2-year post-HSCT and implies that earlier GVHD-associated mortality may be offset by later relapse mortality producing similar overall survival over time.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".