Risk factors for post‐transplant Epstein‐Barr virus events in pediatric recipients of hematopoietic stem cell transplants
Bibliographic record
Abstract
BACKGROUND: Epstein-Barr virus (EBV) can cause severe disease following hematopoietic stem cell transplant (HSCT), including post-transplant lymphoproliferative disorder (PTLD). The objective was to analyze risk factors associated with post-transplant EBV outcomes among pediatric allogeneic HSCT recipients. METHODS: We used data from 156 pediatric allogeneic HSCT recipients enrolled in the Canadian multicenter TREASuRE study. Cox and Prentice-Williams-Petersen models were used to analyze risk factors for post-transplant EBV events including occurrence and recurrence of EBV DNAemia, increase in EBV viral load (EBV-VL), and preemptive use of rituximab, an effective therapy against PTLD. RESULTS: Females were at higher risk for increasing EBV-VL (adjusted hazard ratio (HR) = 2.83 [95% confidence intervals (CI): 1.33-6.03]) and rituximab use (HR = 3.08 [1.14-8.30]), but had the same EBV DNAemia occurrence (HR = 1.21 [0.74-1.99]) and recurrence risks (HR=1.05 [0.70-1.58]) compared to males. EBV DNAemia was associated with recipient pre-transplant EBV seropositivity (HR = 2.47 [1.17-5.21]) and with graft from an EBV-positive donor (HR = 3.53 [1.95-6.38]). Anti-thymocyte globulin (ATG) was strongly associated with all EBV outcomes, including the use of rituximab (HR = 5.33 [1.47-19.40]). Mycophenolate mofetil (MMF) significantly decreased the risk of all EBV events including the rituximab use (HR = 0.13 [0.03-0.63]). CONCLUSION: This study in pediatric allogeneic HSCT patients reveals a reduced risk of all EBV outcomes with the use of MMF. Risk factors for EBV events such as EBV-VL occurrence and recurrence include EBV positivity in the donor and recipient, and use of ATG, whereas risk factors for the most severe forms of EBV outcome (EBV-VL and the use of rituximab) include female sex and ATG use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".