The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology
Bibliographic record
Abstract
The XIII Banff meeting, held in conjunction the Canadian Society of Transplantation in Vancouver, Canada, reviewed the clinical impact of updates of C4d-negative antibody-mediated rejection (ABMR) from the 2013 meeting, reports from active Banff Working Groups, the relationships of donor-specific antibody tests (anti-HLA and non-HLA) with transplant histopathology, and questions of molecular transplant diagnostics. The use of transcriptome gene sets, their resultant diagnostic classifiers, or common key genes to supplement the diagnosis and classification of rejection requires further consensus agreement and validation in biopsies. Newly introduced concepts include the i-IFTA score, comprising inflammation within areas of fibrosis and atrophy and acceptance of transplant arteriolopathy within the descriptions of chronic active T cell-mediated rejection (TCMR) or chronic ABMR. The pattern of mixed TCMR and ABMR was increasingly recognized. This report also includes improved definitions of TCMR and ABMR in pancreas transplants with specification of vascular lesions and prospects for defining a vascularized composite allograft rejection classification. The goal of the Banff process is ongoing integration of advances in histologic, serologic, and molecular diagnostic techniques to produce a consensus-based reporting system that offers precise composite scores, accurate routine diagnostics, and applicability to next-generation clinical trials.
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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 | 0.048 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".