Incidence, risk factors, outcomes, and clinical management of BK viremia in the modern era of kidney transplantation
Bibliographic record
Abstract
Abstract BK viremia is endemic among kidney transplant recipients (KTRs). Incidence, risk factors, outcomes, and clinical management of detectable versus high BK viremia have not been considered previously in KTR in the modern era. This observational study examined KTR transplanted between January 1, 2009 and December 31, 2016. Any BK viral load in the serum constituted detectable BK viremia and ≥10 3 copies/ml constituted high viremia. Among 1193 KTRs, the cumulative probability of developing detectable and high BK viremia within 2 years posttransplant were 27.8% and 19.6%, respectively. Significant risk factors for detectable BK viremia included recipient age (HR 1.02 [95% CI: 1.01, 1.03]) and donor age (HR 1.01 [95% CI: 1.00, 1.02]). Recipient age also predicted high BK viremia (HR 1.02 [95% CI: 1.01, 1.03]), whereas White race (HR 0.70 [95% CI: 0.52, 0.95]), nondepleting induction therapy (HR 0.61 [95% CI: 0.42, 0.89]), and delayed graft function (HR 0.61 [95% CI: 0.42, 0.88]) were protective. Mean estimated glomerular filtration rates were 4.28 ml/min/1.72 m 2 (95% CI: 2.71, 5.84) lower with detectable BK viremia. Although low viral load was usually not acted upon at first presentation, antiproliferative dose reductions were the most common initial management. BK viremia remains a common early complication in a modern cohort of KTRs. These findings highlight the benefit of early BKV monitoring in addition to intensive clinical management. Clinical responses beyond first positive BK viremia tests, and their implications for graft outcomes, merit further investigation. image
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".