Screening Algorithm for BK Virus-Associated Nephropathy Using Sequential Testing of Urinary Cytology: A Probabilistic Model Analysis
Bibliographic record
Abstract
BACKGROUND: Incorporating urinary cytology in BK virus (BKV) screening algorithm potentially reduces the screening cost for BK viral nephropathy. We aimed to evaluate the test performances and screening cost of sequential 2-stage screening consisting of urine cytology followed by BKV serum quantitative polymerase chain reaction (PCR). METHODS: Ninety-five kidney transplant recipients who had BKV serum quantitative PCR/urine cytology tested and verified with histopathology (the reference gold standard) were included. A probabilistic model was constructed to evaluate the test performance and screening cost of 2-stage screening, and was compared with screening with urine cytology or serum viral load alone. RESULTS: At a viral load threshold of ≥104 copies/ml, the sensitivity and specificity of quantitative PCR alone were 83% (95% CI 69-96) and 91% (95% CI 83-97), respectively. The sensitivity and specificity of urine cytology alone were 91% (95% CI 79-100) and 74% (95% CI 60-91), respectively. Sequential 2-stage screening resulted in loss in sensitivity but a net gain in specificity (viral load threshold ≥104 copies/ml - sensitivity, 75% (95% CI 60-91); specificity, 98% (95% CI 95-99)). Two-stage screening also had superior positive predictive value and is cost effective when BKV-associated nephropathy prevalence is below 94%. CONCLUSIONS: Our study had demonstrated a favorable test performance and cost efficiency of 2-stage BKV screening.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".