Statin Use and Bone Mineral Density in Renal Transplant Recipients
Bibliographic record
Abstract
Chronic renal dysfunction is common in non-renal solid organ (SOT) and hematopoietic stem cell transplant (HSCT) recipients and is commonly attributed to calcineurin inhibitor toxicity, often without renal histopathologic evaluation. Polyomavirus nephropathy (PVN) is an important cause of allograft dysfunction in kidney transplant recipients but has rarely been reported in native kidneys of non-renal transplant recipients. We report the clinical, pathologic and virologic features of PVN in native kidneys of two allograft recipients. In both, severe renal dysfunction was accompanied by histopathologic evidence of PVN, including characteristic viral inclusions by routine stains, immunohistochemistry and electron microscopy. High levels of BK virus (BKV) DNA were detected in kidney tissue of patients using BKV-specific polymerase chain reaction (PCR). In 1 patient, high levels of BKV DNA were detected in plasma and urine, and administration of low-dose cidofovir was associated with clearance of BK viremia. These results extend the populations in which PVN has been documented in native kidneys to include heart and stem cell transplant recipients, and suggest that cidofovir has activity against BKV in vivo. Studies to define the incidence and potential contribution of PVN to chronic renal dysfunction commonly attributed to calcineurin inhibitor toxicity in non-renal transplant recipients are warranted.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".