A Multicenter Application of the 2018 Banff Classification for BK Polyomavirus-associated Nephropathy in Renal Transplantation
Bibliographic record
Abstract
BACKGROUND: With current immunosuppressive regimens, BK polyomavirus-associated nephropathy (BKPyVAN) is still a matter of concern. Stratification of patients at risk for allograft loss is of uttermost importance to guide treatment choice and assess prognosis. In 2018, the Banff working group proposed a classification scheme for the prognosis of BKPyVAN, but external application on independent cohorts is yet to be performed. We investigated how the 2018 Banff classification would perform in a multicenter cohort comprising 50 cases of biopsy-proven BKPyVAN compared to previously published classification systems. METHODS: We analyzed consecutive BKPyVAN cases from two Dutch university hospitals between 2002 and 2013, retrieved clinical data, and scored all biopsies according to the Banff 2018 classification, and as a comparison, 4 previously proposed BKPyVAN classification systems. We used estimated glomerular filtration rate trajectories and death-censored graft survival as primary endpoints. RESULTS: The 2018 Banff classification did not associate with estimated glomerular filtration rate decline or graft failure and performed only slightly better than the 4 previously proposed classifiers. Anti-human leukocyte antigen donor-specific antibodies (DSAs), especially in combination with ongoing biopsy-proven BKPyVAN on follow-up, did correlate with graft function and survival. Patients who were DSA+/BKPyVAN+ on follow-up had more inflammation at the baseline biopsy, which by itself was not associated with graft outcomes. CONCLUSIONS: Neither the 2018 Banff BKPyVAN classification nor previously published stratification systems could be applied to our multicenter patient cohort. Our data suggest that there might be a prognostic value for follow-up biopsies and DSA measurements to improve risk stratification after BKPyVAN, although prospective multicenter efforts with protocol measurements are needed to confirm this.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".