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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".