Banff Schema for Grading Pancreas Allograft Rejection: Working Proposal by a Multi-Disciplinary International Consensus Panel
Bibliographic record
Abstract
Accurate diagnosis and grading of rejection and other pathological processes are of paramount importance to guide therapeutic interventions in patients with pancreas allograft dysfunction. A multi-disciplinary panel of pathologists, surgeons and nephrologists was convened for the purpose of developing a consensus document delineating the histopathological features for diagnosis and grading of rejection in pancreas transplant biopsies. Based on the available published data and the collective experience, criteria for the diagnosis of acute cell-mediated allograft rejection (ACMR) were established. Three severity grades (I/mild, II/moderate and III/severe) were defined based on lesions known to be more or less responsive to treatment and associated with better- or worse-graft outcomes, respectively. The features of chronic rejection/graft sclerosis were reassessed, and three histological stages were established. Tentative criteria for the diagnosis of antibody-mediated rejection were also characterized, in anticipation of future studies that ought to provide more information on this process. Criteria for needle core biopsy adequacy and guidelines for pathology reporting were also defined. The availability of a simple, reproducible, clinically relevant and internationally accepted schema for grading rejection should improve the level of diagnostic accuracy and facilitate communication between all parties involved in the care of pancreas transplant recipients.
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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 | 0.134 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.016 | 0.007 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".