Transplant nephropathology: Wherefrom, wherein, and whereto
Bibliographic record
Abstract
Renal pathology is a relatively recent entry in nephrology. While diseases of the kidney are old, their study began in the 19th century with the report of Richard Bright of the lesions of end-stage kidney disease. Its easy diagnosis from albuminuria soon elevated Bright's nephritis into a leading cause of death. The transformative events in the care of these cases were renal replacement therapy that converted a fatal into a chronic disease, and kidney biopsy that allowed study of the course and pathogenesis of kidney disease. Apart from its fundamental contributions to clinical nephrology, biopsy of renal allografts became an integral component of the evaluation and care of kidney transplant recipients. The Banff transplant pathology conferences launched in 1991 led to developing the classification of allograft pathology into an essential element in the evaluation, treatment, and care of allograft recipients with spirit of discovery. That success came at the cost of increasing complexity leading to the recent realization that it may need the refinement of its consensus-based system into a more evidence-based system with graded statements that are easily accessible to the other disciplines involved in the care of transplanted patients. Collaboration with other medical disciplines, allowing public comment on meeting reports, and incorporation of generative artificial intelligence (AI) are important elements of a successful future. The increased pace of innovation brought about by AI will likely allow us to solve the organ shortage soon and require new classifications for xenotransplantation pathology, tissue engineering pathology, and bioartificial organ pathology.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".