Evaluation of Biopsy-Based Molecular Risk Prediction in Crescentic Glomerulonephritis
Bibliographic record
Abstract
Introduction: Novel molecular tools have the potential to improve current clinical and histology-based risk classification systems for various medical renal diseases including glomerulonephritis (GN). We aimed to assess the utility of gene expression for improving biopsy-based risk prediction in patients with GN with and without crescent formation. Methods: This retrospective case-control study used NanoString nCounter to measure the expression of 54 previously described inflammation, nephron injury, endothelium, and crescent-related genes in 335 archival, formalin-fixed paraffin-embedded native kidney biopsies, including a 288-biopsy discovery cohort representing a broad spectrum of crescentic GN subtypes, and an independent 47-biopsy validation cohort focused on ANCA-associated crescentic GN. Clinical, histologic, and gene expression data were compared. Results: Discovery cohort analysis demonstrated increased expression of 13 genes in crescentic GN cases that developed end-stage renal disease (ESRD) versus those that did not (false discovery rate <0.05). Within the 75-biopsy subset of ANCA-associated crescentic GN cases in the discovery cohort, this 13-gene set was found to be independently predictive of ESRD in multivariate Cox proportional hazards regression analysis (p = 0.015), with significant differentiation of high and low risk patients in the Kaplan-Meier renal survival analysis (log-rank test, p = 0.002). However, validation cohort analysis did not demonstrate significant improvement in risk stratification with the 13-gene set when compared with established clinicopathologic models. Conclusion: These results suggest that biopsy-based gene expression may provide the opportunity for improved risk stratification in crescentic GN; however, the genes evaluated in this study appear to have limited added clinical utility over existing risk scores.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".