Epstein‐Barr virus associated smooth muscle tumors in solid organ transplant recipients: Incidence over 31 years at a single institution and review of the literature
Bibliographic record
Abstract
INTRODUCTION: Epstein-Barr virus (EBV) associated smooth muscle tumors (EBV-SMT) are a rare complication of solid organ transplantation (SOT). Incidence data related to this EBV-SMT are limited. EBV DNA is universally present in these tumors. How these cells get infected with EBV, whether this is a result of primary EBV infection vs reactivation, and how persistent active EBV infection post-transplant influences EBV-SMT pathogenesis remains unknown. METHODS: Among 5006 SOT recipients (474 pediatric, 4532 adult) receiving SOT at our center between Jan 1984 and Dec 2015, three cases of post-transplant EBV-SMT were identified. RESULTS: All cases were pediatric heart transplants who were EBV seronegative prior to transplant, and experienced primary EBV infection with persistently elevated EBV viral loads, despite antiviral therapy. Two are deceased at 3.2 and 0.9 years post-diagnosis, while one remains alive 6.2 years post diagnosis. The overall local incidence of post-transplant EBV-SMT at our institution was 0.7 (95% CI, 0.2-1.7) per 1000 patient years, and 2.6 (95% CI, 0.6-6.7) per 1000 patient years in pediatric heart transplants. A literature review identified 36 pediatric and 51 adult cases of post-transplant EBV-SMT. CONCLUSIONS: We hypothesize that pre-transplant EBV seronegativity, followed by primary EBV infection and persistently high EBV viral loads, represents a unique risk factor for post-transplant EBV-SMT. Pediatric heart transplant recipients were found to be disproportionately affected by post-transplant EBV-SMT at our institution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".