Risk Factors for BK Polyoma Virus Treatment and Association of Treatment With Kidney Transplant Failure
Bibliographic record
Abstract
BACKGROUND: Identification of risk factors for BK polyoma virus (BKPyV) without confounding by donor factors and era effects in paired analysis may inform strategies to prevent BKPyV. METHODS: In this analysis of 21,575 mate kidney pairs in the Scientific Registry of Transplant Recipients between 2004 and 2010, the presence of a treatment code for BKPyV virus in follow-up forms was used to identify pairs in which 1 of 2 mate kidneys was treated (discordant treatment) or both mate kidneys were treated (concordant treatment). RESULTS: Among 1975 discordant pairs, younger than 18 years or 60 years or older, male sex, HLA mismatch or 4 greater, acute rejection, and depleting antibody induction had a higher odds of treatment, whereas diabetes and sirolimus had a lower odds of treatment, and treatment was associated with a higher risk of allograft failure (hazards ratio, 2.01; 95% confidence interval, 1.63-2.48). The rate of concordant treatment (0.81%) was 2.8 times higher than expected. Concordant treatment was associated with nonwhite donor ethnicity, donation after circulatory death, transplantation after 2008, and transplantation of mate kidneys in the same center. CONCLUSIONS: This analysis of kidneys from the same donor in which only 1 transplant was treated for BKPyV identifies specific risk factors (age <18 or ≥ 60 years, male sex, depleting antibody, HLA mismatch ≥ 4) for BKPyV and provides an estimate of the BKPyV-associated risk of allograft failure (hazards ratio = 2.01) without confounding by donor factors or era effects. The higher than expected rate of concordant treatment suggests the importance of donor factors in BKPyV pathogenesis and warrants further study.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".