Continuous indices to assess the phenotypic spectrum of kidney transplant rejection
Bibliographic record
Abstract
The Banff classification for kidney transplant pathology dichotomizes the rejection continuum into distinct diagnostic categories, introducing artificial cutoff points and threshold effects. To better reflect the underlying disease spectrum, in this cohort study of 19,500 biopsies from 8873 patients across 10 centers worldwide, we developed two indices for quantifying antibody-mediated rejection/microvascular inflammation and T-cell-mediated rejection/tubulointerstitial inflammation from histological lesion scores and calculated indices for overall activity and chronicity. These indices demonstrate excellent discrimination for the main diagnostic categories of rejection (AUCs from 0.95 to 0.99), with consistent performance across derivation and validation datasets. These indices strictly confine intermediate phenotypes to low index values and are associated to graft failure even within the diagnostic categories, thus reflecting the underlying rejection continuum. In this work, we demonstrate that four continuous indices provide implementable and interpretable global evaluation of kidney transplant histology that align with the continuous nature of the rejection process regardless of the underlying disease cause.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".