The Role of Antiviral Prophylaxis for the Prevention of Epstein–Barr Virus–Associated Posttransplant Lymphoproliferative Disease in Solid Organ Transplant Recipients: A Systematic Review
Bibliographic record
Abstract
The role of antiviral prophylaxis for the prevention of posttransplant lymphoproliferative disease (PTLD) remains controversial for solid organ transplantation (SOT) recipients who are seronegative for Epstein-Barr virus (EBV) but who received organs from seropositive donors. We performed a systematic review and meta-analysis to address this issue. Two independent assessors extracted data from studies after determining patient eligibility and completing quality assessments. Overall, 31 studies were identified and included in the quantitative synthesis. Nine studies were included in the direct comparisons (total 2366 participants), and 22 were included in the indirect analysis. There was no significant difference in the rate of EBV-associated PTLD in SOT recipients among those who received prophylaxis (acyclovir, valacyclovir, ganciclovir, valganciclovir) compared with those who did not receive prophylaxis (nine studies; risk ratio 0.95, 95% confidence interval 0.58-1.54). No significant differences were noted across all types of organ transplants, age groups, or antiviral use as prophylaxis or preemptive therapy. There was no significant heterogeneity in the effect of antiviral prophylaxis on the incidence of PTLD. In conclusion, the use of antiviral prophylaxis in high-risk EBV-naive patients has no effect on the incidence of PTLD in SOT recipients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".