Archetypal analysis of deceased donor kidneys: A molecular approach for posttransplant outcomes
Bibliographic record
Abstract
Donor kidney tissue-based transcriptomics may represent a new dimension for the prediction of kidney transplant outcomes. In this prospective, single-center study, 276 kidneys from 174 deceased brain-death donors were assessed by microarrays to identify phenotypes of procurement biopsies. Molecular classifiers (extreme gradient boosting, logistic, and Poisson regression) with 10-fold cross-validation were employed to categorize donors based on clinical variables (age, body mass index, hypertension, expanded criteria donor kidney) and histologic scores (vascular fibrous intimal thickening, interstitial fibrosis, tubular atrophy, arteriolar hyaline thickening). Archetypal analysis and linear mixed model were applied to determine molecular phenotypes and their association with 1-year posttransplant estimated glomerular filtration rate (eGFR) in 234 donor kidneys. Three molecular archetypes were identified. The "ideal" archetype (median donor age 42 years, low Kidney Donor Risk Index [KDRI], minimal chronic histologic changes) was associated with the highest 1-year eGFR, whereas the "marginal" archetype (68 years, extensive chronic changes, high KDRI) was associated with the lowest one. The "intermediate" archetype yielded better 1-year eGFR despite donor profiles similar to the marginal group. Although KDRI predicted 1-year eGFR, adding molecular archetypes improved model performance (Akaike Information Criterion [AIC] 80.0 vs 83.7; P < .05). External validation in an independent data set (n = 174, GSE147451) confirmed the predictive value of the model. Molecular profiling of procurement biopsies may help to identify donor kidneys with higher posttransplant eGFR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".