Post‐transplant lymphoproliferative disorders, Epstein‐Barr virus infection, and disease in solid organ transplantation: Guidelines from the American Society of Transplantation Infectious Diseases Community of Practice
Bibliographic record
Abstract
Abstract These updated guidelines from the American Society of Transplantation Infectious Diseases Community of Practice review the diagnosis, management, and prevention of post‐transplant lymphoproliferative disorders (PTLD) and other Epstein‐Barr virus (EBV) syndromes after solid organ transplantation. PTLD are a heterogeneous spectrum of predominantly B‐cell disorders, often extra‐nodal, with complex distinct pathogeneses and variable clinical presentations determined by pathologic subtype. Recent epidemiologic studies report a decrease in early EBV‐positive (+) PTLD and an increase in late EBV‐negative (−) PTLD. Pre‐transplant EBV‐seronegativity and primary EBV infection, often from donor‐transmitted infection, are an important risk factors for EBV syndromes and early EBV + PTLD. Low‐quality evidence supports preemptive prevention strategies for early EBV + PTLD in EBV‐seronegative recipients that involve EBV DNA measurement in peripheral blood using assays requiring further result harmonization, combined with interventions to lower viral load. Reduction in immunosuppression (RIS) is the best validated intervention. WHO pathology classification of a tissue biopsy remains the gold standard for PTLD diagnosis; optimal staging procedures are uncertain. Treatment of CD20 + PTLD with the response‐dependent sequential use of RIS, rituximab, and cytotoxic chemotherapy is recommended. Evidence gaps requiring future research and alternate treatment strategies including immunotherapy are highlighted.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".