P0343RISK PREDICTION MODELS FOR PREDICTING PROGRESSION OF IGA NEPHROLOGY:A VALIDATION STUDY
Bibliographic record
Abstract
Abstract Background and Aims Risk prediction models for IgA nephropathy (IgAN) containing clinical variables (clinical model) or clinical plus pathological variables (full model) have been established based on a large international collaborative study recently, but external validation of these models are still required before clinical application. The aim of this study is to externally validate previously reported risk prediction models based on our multi-center IgAN cohort. Method Biopsy-proven IgAN patients with eGFR ≥15 ml/min/1.73 m2 at baseline and a minimum follow-up of 6 months were enrolled. Primary outcome was defined as end-stage kidney disease (ESRD). Cox proportional hazards models were built to validate risk models. R2, Akaike information criterion (AIC) and C statistic were calculated to evaluate model accuracy. Results A total of 2300 IgAN patients with a median follow-up of 30 months were enrolled, and 214(9.3%) ESRD occurred during the follow-up period. The median age was 35(interquartile range, 28-44) years, and 1106 cases (48.1%) were men. Our cohort successfully validated the clinical model and the full model based on C statistic (0.90 and 0.91) and R2 (0.32 and 0.32). Our results showed limited improvement in model performance after adding the Oxford classification parameters to clinical parameters. However, both two models performed better than the model consisting only pathological parameters(C statistic 0.83, R2 0.24). We also validated other risk prediction models, including CLIN model (C statistic 0.90, R2 0.32) and CLINPATH model (C statistic 0.91, R2 0.31) derived from Chinese IgAN patients or CKD model (C statistic 0.90, R2 0.32) derived from Canadian CKD patients. It was found that clinical models based on different combinations of clinical parameters performed similarly. Conclusion In summary, we successfully validated a recently reported IgAN risk model and we found that clinical parameters alone could accurately predict ESRD risk in IgAN patients.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".