Epstein‐Barr virus‐associated smooth muscle tumors after lung transplantation
Bibliographic record
Abstract
BACKGROUND: Recipients of solid organ transplants are prone to various complications that are seldom encountered in immunocompetent individuals. Post-transplant lymphoproliferative disorder (PTLD) is the best known and commonest Epstein-Barr Virus (EBV)-associated malignancy post solid organ transplant. EBV-associated smooth muscle tumors (EBV-SMT) including leiomyomas and leiomyosarcomas are rare and much less studied than PTLD. We recently encountered two cases of EBV-SMT post lung transplantation and here we summarize their clinical features and course together with a literature review. METHOD: Clinical data and treatment details of two patients who developed EBV-SMT were reviewed and retrieved up to December 31, 2017. English literature was searched through the PubMed database from 1965 to 2017 for studies of the association between lung transplant and EBV-SMT. RESULTS: The incidence of PTLD is higher among lung transplant recipients compared to kidney transplant recipients, an observation that has been attributed to stronger immune suppression in the lung patients. EBV-SMT showed a higher incidence among kidney recipients than among lung recipients, suggesting that the degree of immunosuppression may be a less important factor in the development of EBV-SMT. EBV-SMT has most often been seen among lung transplant recipients with EBV mismatch. CONCLUSIONS: Because EBV-SMT is a rare tumor, its incidence, risk factors, and optimal management have not been well-defined and further study is needed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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 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".