An Integrated View of Molecular Changes, Histopathology and Outcomes in Kidney Transplants
Bibliographic record
Abstract
Data-driven approaches to deteriorating kidney transplants, incorporating histologic, molecular and HLA antibody findings, have created a new understanding of transplant pathology and why transplants fail. Transplant dysfunction is best understood in terms of three elements: diseases, the active injury-repair response and the cumulative burden of injury. Progression to failure is mainly attributable to antibody-mediated rejection, nonadherence and glomerular disease. Antibody-mediated rejection usually develops late due to de novo HLA antibodies, particularly anti-class II, and is often C4d negative. Pure treated T cell-mediated rejection does not predispose to graft loss because it responds well, even with endothelialitis, but it may indicate nonadherence. The cumulative burden of injury results in atrophy-fibrosis (nephron loss), arterial fibrous intimal thickening and arteriolar hyalinosis, but these are not progressive without ongoing disease/injury, and do not explain progression. Calcineurin inhibitor toxicity has been overestimated because burden-of-injury lesions invite this default diagnosis when diseases such as antibody-mediated rejection are missed. Disease/injury triggers a stereotyped active injury-repair response, including de-differentiation, cell cycling and apoptosis. The active injury-repair response is the strongest correlate of organ function and future progression to failure, but should always prompt a search for the initiating injury or disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".