Banff Fibrosis Study: Multicenter Visual Assessment and Computerized Analysis of Interstitial Fibrosis in Kidney Biopsies
Bibliographic record
Abstract
Increasing interstitial fibrosis (IF) in native and kidney transplant biopsies is associated with functional decline and serves as a clinical trial end point. A Banff 2009 Conference survey revealed a range in IF assessment practices. Observers from multiple centers were asked to assess 30 renal biopsies with a range of IF and quantitate IF using two approaches on trichrome, Periodic acid-Schiff (PAS) and computer-assisted quantification of collagen III immunohistochemistry (C-IHC) slides, as well as assessing percent of cortical tubular atrophy% (TA%) and Banff total cortical inflammation score (ti-score). C-IHC using whole slide scans was performed. C-IHC assessment showed a higher correlation with organ function (r = -0.48) than did visual assessments (r = -0.32--0.42); computerized and visual C-IHC assessment also correlated (r = 0.64-0.66). Visual assessment of trichrome and C-IHC showed better correlations with organ function and C-IHC, than PAS, TA% and ti-score. However, visual assessment of IF, independent of approach, was variable among observers, and differences in correlations with organ function were not statistically significant among C-IHC image analysis and visual assessment methods. C-IHC image analysis correlated among three centers (r > 0.90, p < 0.0001, between all centers). Given the difficulty of visual IF assessment standardization, C-IHC image could potentially accomplish standardized IF assessment in multicenter settings.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".