Evaluating Progression Risk in Patients With Immunoglobulin A Nephropathy
Bibliographic record
Abstract
The highly variable rate of decline in kidney function in patients with immunoglobulin A nephropathy (IgAN) provides a major clinical challenge. Predicting which patients will progress to kidney failure, and how quickly, is difficult. Multiple novel therapies are likely to be approved in the short-term, but clinicians lack the tools to identify patients most likely to benefit from specific treatments at the right time. Noninvasive and validated markers for selecting at-risk patients and longitudinal monitoring are urgently needed. This review summarizes what is known about demographic, clinical, and histopathologic prognostic markers in the clinician's toolkit, including the International IgAN Prediction Tool. We also briefly review what is known on these topics in children and adolescents with IgAN. Although helpful, currently used markers leave clinicians heavily reliant on histologic features from the diagnostic kidney biopsy and standard clinical data to guide treatment choice, and very few noninvasive markers reflect treatment efficacy over time. Novel prognostic and predictive markers are under clinical investigation, with considerable progress being made in markers of complement activation. Other areas of research are the interplay between gut microbiota and galactose-deficient IgA1 expression; microRNAs; imaging; artificial intelligence; and markers of fibrosis. Given the rate of therapeutic advancement, the remaining gaps in biomarker research need to be addressed. We finish by describing our route to clinical utility of predictive and prognostic markers in IgAN. This route will provide us with the chance to improve IgAN prognosis by using robust, clinically practical markers to inform patient care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".