Banff 2022 pancreas transplantation multidisciplinary report: Refinement of guidelines for T cell–mediated rejection, antibody-mediated rejection and islet pathology. Assessment of duodenal cuff biopsies and noninvasive diagnostic methods
Bibliographic record
Abstract
The Banff pancreas working schema for diagnosis and grading of rejection is widely used for treatment guidance and risk stratification in centers that perform pancreas allograft biopsies. Since the last update, various studies have provided additional insight regarding the application of the schema and enhanced our understanding of additional clinicopathologic entities. This update aims to clarify terminology and lesion description for T cell-mediated and antibody-mediated allograft rejections, in both active and chronic forms. In addition, morphologic and immunohistochemical tools are described to help distinguish rejection from nonrejection pathologies. For the first time, a clinicopathologic approach to islet pathology in the early and late posttransplant periods is discussed. This update also includes a discussion and recommendations on the utilization of endoscopic duodenal donor cuff biopsies as surrogates for pancreas biopsies in various clinical settings. Finally, an analysis and recommendations on the use of donor-derived cell-free DNA for monitoring pancreas graft recipients are provided. This multidisciplinary effort assesses the current role of pancreas allograft biopsies and offers practical guidelines that can be helpful to pancreas transplant practitioners as well as experienced pathologists and pathologists in training.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".