Banff Initiative for Quality Assurance in Transplantation (BIFQUIT): Reproducibility of Polyomavirus Immunohistochemistry in Kidney Allografts
Bibliographic record
Abstract
Immunohistochemistry (IHC) is the gold standard for diagnosing (positive vs. negative) polyomavirus BK (BKV) nephropathy and has the potential for disease staging based on staining intensity and quantification of infected cells. This multicenter trial evaluated the reproducibility of BKV IHC among 81 pathologists at 60 institutions. Participants stained tissue microarray slides and scored them for staining intensity and percentage of positive nuclei. Staining protocol details and evaluation scores were collected online. Slides were returned for centralized panel re-evaluation and kappa statistics were calculated. Individual assessment of staining intensity and percentage was more reproducible than combined scoring. Inter-institutional reproducibility was moderate for staining intensity (κ = 0.49) and percentage (κ = 0.42), fair for combined (κ = 0.25) and best for simple positive/negative scoring (κ = 0.78). Inter-observer reproducibility was substantial for intensity (κ = 0.74), percentage (κ = 0.66), positive/negative (κ = 0.78) and moderate for combined scoring (κ = 0.43). Inter-laboratory reproducibility was fair for intensity (κ = 0.37), percentage (κ = 0.40) and combined (κ = 0.24), but substantial for positive/negative scoring (κ = 0.67). BKV RNA copies/cell correlated with staining intensity (r = 0.56) and percentage (r = 0.62). These results indicate that BKV IHC is reproducible between observers but scoring should be simplified to a single-feature schema. Standardization of tissue processing and staining protocols would further improve inter-laboratory reproducibility.
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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 | 0.288 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".