How should we measure and interpret glomerular inflammation and what is the best anti-inflammatory approach in IgA nephropathy?
Bibliographic record
Abstract
Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis worldwide. The pathogenesis of IgAN is complex, characterized by gut mucosa-kidney interactions that lead to the production of galactose-deficient IgA1 and formation of immune complexes with antiglycan antibodies. Both arms of the adaptive and innate immune system are implicated in modifying or amplifying inflammatory cascades that lead to disease progression. Thus, altering the disease trajectory in IgAN requires therapies that target these underlying inflammatory processes. Broad immunosuppression, including systemic or locally gut mucosa-delivered corticosteroids and mycophenolate mofetil, have demonstrated efficacy in reducing proteinuria, although these are tempered by the risk of adverse effects. More recently, targeted treatment approaches at specific pathways, including complement inhibition and BAFF and/or APRIL inhibitors, have demonstrated promise and are under evaluation. Our ability to measure the degree of glomerular inflammation and predict response to treatment remains limited. To date, the IgA International Risk Prediction Tool remains the gold standard for the prediction of up to 5-year kidney outcomes guided by clinical criteria including proteinuria, estimated glomerular filtration rate and histologic criteria through the MEST-C score. With multiple potential emerging treatments, there is a need for validated biomarkers that reflect the degree of inflammation or IgAN disease activity that may facilitate personalized treatment strategies and improve long-term outcomes.
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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 | 0.058 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".