Incidence and Timing of Epstein–Barr Virus Whole Blood DNAemia in Epstein–Barr Virus‐Mismatched Adult and Pediatric Solid Organ Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Epstein-Barr virus (EBV) viral load (VL) monitoring is recommended post-transplant for EBV-mismatched (donor EBV seropositive/recipient EBV seronegative) solid organ transplant (SOT) recipients as a component of post-transplant lymphoproliferative disorder (PTLD) prevention, but the optimal frequency and timing of EBV VL monitoring remains unknown. METHODS: In this retrospective cohort study, we investigated the incidence and timing of whole blood EBV DNAemia in EBV-mismatched adult and pediatric SOT recipients, who had EBV VL monitoring as part of a pre-emptive approach to PTLD prevention to optimize monitoring algorithms. We explored associations between donor-acquired EBV DNAemia (DA-EBV), defined as EBV DNAemia within 1 year post-transplant, and donor and recipient characteristics, and determined the proportion who developed PTLD. RESULTS: recipients (kidney n = 64, heart n = 75, liver n = 93, lung n = 25); 126/257 (49.0%) developed DA-EBV at a median of 83 days (Q1-Q3: 50-130 days) post-transplant. Incidence of DA-EBV varied by organ and was highest in liver (62.4%) and lowest in heart recipients (28.0%). PTLD was diagnosed in 38/257 (14.8%) EBV-mismatched recipients, 25/162 (15.4%) children, and 13/95 (13.7%) adults. DA-EBV was uncommon in recipients less than 6 months old (3/29, 10.3%) and among recipients less than 12 months with donors less than 12 months (2/29, 6.9%); possible mechanisms of protection other than recipient passive maternal antibody and false-positive donor serostatus are discussed. CONCLUSION: Monitoring for DA-EBV should be focused on months 2-6 post-transplant. Less frequent whole blood EBV VL monitoring is likely a safe option in recipients less than 6 months old and recipients 6-12 months old with donors less than 12 months old.
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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".