An update on predicting renal progression in IgA nephropathy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Immunoglobulin A (IgA) nephropathy (IgAN) is a heterogeneous disease, and predicting individual patient risk of renal progression is challenging. Recent studies provide new evidence regarding the use of clinical, histologic, and biomarker predictors of renal outcome in IgAN. RECENT FINDINGS: A meta-analysis of clinical trials demonstrated that early change in proteinuria is a valid surrogate outcome measure for longer term decline in renal function, which supports the use of proteinuria to dynamically re-evaluate patient prognosis over time. The MEST histologic classification has been further validated in a large European cohort. An international multiethnic observational study demonstrated that crescents are independently associated with renal outcome, and as a result a crescent score (<25% versus >25% of glomeruli) has been added to MEST. Proteinuria, estimated glomerular filtration rate (GFR), and blood pressure at the time of biopsy can be used to accurately predict prognosis when combined with MEST, instead of using 2 years of follow-up data. Currently, no available risk prediction model that combines clinical and histologic predictors has been sufficiently validated for routine use. There are multiple biomarkers that have been studied in IgAN, however none have been externally validated and shown to improve prediction beyond clinical and histologic risk factors. SUMMARY: Proteinuria, estimated GFR, blood pressure, and the MEST-C score are the most readily available risk factors to predict renal prognosis in IgAN. Future research is required to develop and validate methods of integrating these risk factors together to accurately risk stratify individual patients, and provide the framework for evaluating biomarkers capable of further improving risk prediction.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".