Continuous indices to assess the phenotypic spectrum of kidney transplant rejection
Bibliographic record
Abstract
The international Banff classification for kidney transplant pathology discretizes the rejection continuum into distinct diagnostic categories, introducing artificial dichotomization and threshold effects. To better reflect the underlying disease spectrum, we developed, in this cohort study, two novel indices for quantifying antibody-mediated (AMR) and T-cell mediated rejection (TCMR) from histological lesion scores, and calculated indices for overall activity and chronicity. These indices were evaluated in one derivation cohort and two independent validation cohorts, totaling 19,500 biopsies from 8,873 kidney transplant patients across 10 centers worldwide. The AMR, TCMR, and activity indices demonstrated hierarchical ordering between No rejection, intermediate and complete rejection histology. The chronicity index showed limited association with the major diagnostic categories. In the derivation cohort, the AMR and TCMR indices discriminated AMR from absence of AMR, and TCMR from absence of TCMR, with an AUC of 0.98 (95% confidence interval 0.97 to 0.98) and 0.99 (0.99 to 1.00) respectively). This excellent discrimination was confirmed in the validation cohorts. Those indices strictly confined intermediate phenotypes to a range of low index values and related to graft failure rates even within the diagnostic categories, thus reflecting the underlying rejection continuum. The four continuous indices offer an implementable and interpretable global evaluation of kidney transplant biopsy histology while eliminating the need for intermediate diagnostic categories and enable more probabilistic reasoning in the diagnostic approach to the spectrum of kidney transplant rejection.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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".