Thirty years of the International Banff Classification for Allograft Pathology: the past, present, and future of kidney transplant diagnostics
Bibliographic record
Abstract
2021 marks the 30th anniversary of the original development of the Banff Classification of Kidney Allograft Pathology, when in August 1991 a group of pathologists and transplant clinicians led by Kim Solez and Lorraine Racusen met in Banff, Alberta, Canada, and established the first widely accepted criteria for the diagnosis of kidney transplant rejection and other lesions seen on kidney allograft biopsies. Since that time, Banff conferences have been held every 2 years at many sites around the world, resulting in several major revisions to the classification and expansion well beyond pure histopathology of kidney allografts to encompass other solid organ transplants, and with involvement of immunogeneticists, immunologists, other basic scientists, biostatisticians, and data scientists defining a very diverse and integrated Banff community. This approach with multidisciplinary international input, constantly incorporating new evidence from the scientific literature and from studies performed by Banff working groups while still maintaining the importance of a long-standing consensus process, has resulted in the Banff classification gaining overwhelming international acceptance as the main reference used for the scoring of kidney allograft biopsies in research studies, routine practice, and clinical trials. This review focuses on the major milestones in the development of the Banff classification of kidney allograft pathology and the evolution of the Banff process over the past 3 decades, with prospects for future advances and refinements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".