External Validation of International Risk-Prediction Models of IgA Nephropathy in an Asian-Caucasian Cohort
Bibliographic record
Abstract
Introduction Two prediction models for IgA nephropathy (IgAN) using clinical variables and the Oxford MEST scores were developed and validated in 2 multiethnic cohorts. Additional external validation is required. Methods Biopsy-proven Chinese and Argentinian patients with IgAN were included. The primary outcome was defined as a 50% decline in estimated glomerular filtration rate (eGFR) or end-stage renal disease. C-statistics and stratified analyses were used for model discrimination, coefficient of determination (R 2 D ) for model fit, and calibration plots for model calibration. Baseline survival function was also evaluated. Results A total of 1275 patients were enrolled, with a mean age of 34 (interquartile range: 27–42) years, 50% of whom (638 of 1275) were men. Use of renin-angiotensin system blockers was higher than in previously reported cohorts, whereas other variables were comparable. The C-statistic of the models was 0.81, and R 2 D was higher than reported. Survival curves in the subgroups (<16th, ∼16th to <50th, ∼50th to <84th, and ≥84th percentiles of linear predictor) were well separated. Most of the predictor variables, including hazard ratio, predicted 5-year risk, and eGFR decline slope, were worse with risk increasing. The baseline survival function was comparable in our cohort and the reported cohorts. The calibration was acceptable for the full model without race. However, the risk probability over 3 years was overestimated in the full model with race included. Conclusion The prediction models showed good performance on personalized risk assessment, which may be used as drug-specific, precision-medicine approaches to treatment decisionmaking.
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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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".