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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".