Validation of the Antineutrophil Cytoplasmic Antibody Renal Risk Score and Modification of the Score in a Chinese Cohort With a Majority of Myeloperoxidase-Positive Patients
Bibliographic record
Abstract
Objective We aimed to validate and modify the renal risk score for antineutrophil cytoplasmic antibody (ANCA)-associated glomerulonephritis (AAGN) in a Chinese cohort with a majority of myeloperoxidase (MPO)-positive patients. Methods A total of 285 patients with biopsy-proven AAGN in our center were retrospectively included. Patients were randomly assigned to the development set (n = 201) and the validation set (n = 84). We calculated the renal risk score and analyzed the clinicopathological characteristics and follow-up data. The nomogram was constructed based on the independent prognostic factors identified by the multivariable Cox regression and then compared with the renal risk score. Results Over a median follow-up period of 41.3 (range 20.0-63.8) months, 84 (29.5%) patients reached end-stage kidney disease (ESKD). In the development set, hypertension (hazard ratio [HR] 2.16, 95% CI 1.08-4.32,P= 0.03), high serum creatinine (HR 1.002, 95% CI 1.001-1.003,P< 0.001), high daily urine protein (HR 1.34, 95% CI 1.15-1.57,P< 0.001), high glomerular sclerosis (HR 13.98, 95% CI 3.50-55.92,P< 0.001), and interstitial fibrosis > 50% (HR 4.18, 95% CI 1.90-9.19,P< 0.001) were independent risk factors for ESKD, and these indicators were included in the nomogram. The C-indices of the nomogram model in the development set, validation set, and all-data set were 0.838 (range 0.785-0.891), 0.794 (range 0.774-0.814), and 0.822 (range 0.775-0.869), respectively, which were higher than those of the renal risk score model, 0.801 (range 0.748-0.854), 0.746 (range 0.654-0.838) and 0.783 (range 0.736-0.830), respectively. The net reclassification improvement and the integrated discrimination improvement further illustrated the higher predictive ability of the nomogram. Conclusion We present a nomogram as a practical tool to predict renal outcomes in Chinese patients with MPO-ANCA glomerulonephritis.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".