Primary Epstein–Barr virus infection, seroconversion, and post‐transplant lymphoproliferative disorder in seronegative renal allograft recipients: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Epstein-Barr virus (EBV)-seronegative renal transplant recipients are at risk of post-transplant lymphoproliferative disorder (PTLD). We compared primary EBV infection, seroconversion, and PTLD in EBV-seronegative patients who received renal allograft from seropositive or seronegative donors (D+/R- and D-/R-, respectively). METHODS: We prospectively followed 25 D+/R- and 8 D-/R- recipients. We followed patients from January 1999 to June 2009 with clinical visits, monthly EBV polymerase chain reaction tests, and serologic tests for a period of 1 year after kidney transplantation and on an individual basis thereafter. RESULTS: Three patients (9%) developed PTLD including 2 early-onset (<12 months) and 1 late-onset (>12 months) disease. In D+/R- and D-/R- patients, the frequencies of PTLD (8% vs. 12.5%, P = 0.7), EBV seroconversion (64% vs. 50%, P = 0.4), and EBV viremia (40% vs. 25%, P = 0.6) were not significantly different. Clinical, serologic, and virologic surveillance as well as reduction in immunosuppression after evidence of primary EBV infection resulted in a PTLD rate of 9%, despite a seroconversion rate of 60.6%. Rate of graft loss after reduction in immunosuppression was 10% (2 of 20), which was not significantly different from 13 patients without EBV seroconversion (no graft loss, P = 0.5). Rates of viremia, seroconversion, and PTLD in D+/R- and D-/R- patients appear to be similar. CONCLUSIONS: The incidence of PTLD in renal transplants ranges from 0.5% to 2.9%. Our data show a significantly higher rate in EBV-seronegative renal allograft recipients, suggesting the need for close surveillance. Our data also suggest that donors for EBV-seronegative recipients may be accepted irrespective of positive or negative serostatus, with ongoing surveillance important in either circumstance.
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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) | 0.001 | 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".