SaO013ARCHETYPE ANALYSIS IDENTIFIES DISTINCT PROFILES IN RENAL TRANSPLANT RECIPIENTS WITH TRANSPLANT GLOMERULOPATHY ASSOCIATED WITH ALLOGRAFT SURVIVAL
Bibliographic record
Abstract
INTRODUCTION: Transplant glomerulopathy (TG) is a common glomerular lesion observed after kidney transplantation associated with bad prognosis. However, TG is not a specific entity but rather the end-stage of overlapping disease pathways. The heterogeneity of TG has not been precisely characterized to date. In this study, we applied a probabilistic data-driven unsupervised approach to a comprehensively phenotyped multicenter cohort. We hypothesized that by deconstructing data matrixes that include pathologic data and serologic and clinical information correlated with meaningful clinical and outcome features, new insights in TG can be gained. METHODS: Consecutive kidney transplant recipients from 3 Paris centers Necker, Saint-Louis and Foch hospitals and 1 center in Edmonton, Alberta presenting with a diagnosis of TG (Banff cg score≥1 by light microscopy) in biopsies performed between January 2004 and January 2014 were included. Comprehensive pathology, clinical, immunological, and outcome data were used in unsupervised archetype analysis. RESULTS: Among the 8,207 post-transplant allograft biopsies performed during the inclusion period, 552 presented with TG (incidence of 6.7%). The median time to TG diagnosis posttransplant was 33.18 months (IQR: 12.12 – 78.72 months). Kidney allograft survival rates after TG diagnosis were 69.4%, 57.1%, 43.3% and 25.5% at 3, 5, 7 and 10 years, respectively. An unsupervised learning method integrating clinical, functional, immunological and histological parameters revealed 5 TG archetypes characterized by distinct functional, immunological, and histological features and associated etiologies. The 5 TG archetypes displayed distinct allograft survival profiles with incremental graft loss rates between archetypes, ranging from 88% to 22% allograft survival rates 5 years after TG diagnosis (p<0.0001). CONCLUSIONS: A probabilistic data-driven archetypical approach applied in a large well-defined multicentric cohort refines the diagnostic and prognostic features associated with TG. Reducing heterogeneity among TG cases can improve disease characterization, enable patient-specific risk stratification, and open new avenues for archetype-based treatment strategies in TG.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".