Clinical and Histological Predictors of Outcomes in C3 Glomerulopathy
Bibliographic record
Abstract
Background: C3 glomerulopathy (C3G) is a rare disease that has significant overlap with idiopathic membranoproliferative glomerulonephritis (MPGN). Risk factors for kidney outcomes remains poorly defined, limited by small cohorts. We aimed todetermine risk factors for kidney outcomes and to incorporate them into a prediction model Methods: Using a cohort of 225 patients with C3G or idiopathic MPGN from three international centers, we evaluated the association between clinical and histologic variables at biopsy and the composite outcome of 30% decline in eGFR or ESKD using Cox proportional hazards models. A prediction model was derived and internally validated through bootstrap resampling Results: In a multivariable model, lower eGFR, paraprotein presence, and interstitial fibrosis were associated with higher risk of outcome, while native (versus transplant) disease and lower C4 levels were associated with lower risk (Table1). The prediction model including these variables performed well (R2D: 53.14%, C-statistic: 0.84 (95% CI 0.82-0.86), integrated calibration index: 0.31), maintaining robustness after internal validation. Adding proteinuria over time showed that a 50% reduction from baseline to <1g/day was associated with a lower risk of outcome (HR 0.35, 95% CI 0.12-0.97) Conclusion: In the largest C3G/MPGN study to date, baseline eGFR, paraprotein, interstitial fibrosis, low C4 and transplant status were independently associated with kidney outcome. These factors can be used in a prediction model to predict individual patient risk. A 50% reduction in proteinuria to <1g/day was associated with lower kidney risk, suggesting it may be an evidence-based definition of proteinuria remission to use in clinical trials Covariates for the risk of progression to the composite outcome in a multivariable model - Parameter Adjusted HR (95% CI) P value eGFR (per 1 log base 2-unit decrease) 2.11 (1.66-2.67) <0.001 Native kidney (versus transplant) 0.35 (0.20-0.60) <0.001 Paraprotein 4.42 (2.00-9.77) <0.001 Low C4 0.51 (0.29-0.90) 0.02 Interstitial fibrosis 1 2.03 (1.03-4.01) 0.04 2 3.01 (1.56-5.83) 0.001 3 4.68 (2.05-10.68) <0.001 Interstitial fibrosis score: 0 (<10%), 1 (10%-25%), 2 (26%-50%) and 3 (>50%)
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".