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 ≥103 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 m2 (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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".