Recent advances in risk prediction, therapeutics and pathogenesis of IgA nephropathy
Bibliographic record
Abstract
Immunoglobulin A nephropathy (IgAN) is the world's commonest primary glomerular disease with variable clinical presentation and progression rates that are dependent on clinical-pathologic phenotype and duration of follow-up. Overall 4-40% of patients progress to end-stage kidney disease (ESKD) by 10 years. Treatment decisions remain a challenge due to these variations. The ultimate goal of management is to prevent progression to ESKD and of vital importance is the potential reversible early detection of active glomerular inflammation prior to scarring. IgAN is globally, is the most common biopsy proven glomerulonephritis and a leading cause of ESKD. The Oxford pathological classification was devised by a collaborative pathology and nephrology network to provide an evidence-based scoring system with reproducible independent pathology features of predictive value. Clinical variables that alter prognosis include male sex, increasing age, increased body weight, smoking, Pacific Asian ethnicity, hypertension, proteinuria, and complement deficiency. Excellent conservative therapy is the cornerstone of therapy with tight blood control, renin-angiotensin system inhibition, and statin therapy. The role of immunosuppressive therapy including corticosteroids in IgAN remains open with ongoing clinical trials of low dose oral corticosteroids and enteric coated budesonide. Complement activation contributes to the pathogenic process of IgAN with evidence from genetic, serological, histological and in-vitro studies. This knowledge has translated to clinical trials of investigational agents directly targeting the alternative pathway.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".