Treatment Patterns Among Adults and Children With Membranous Nephropathy in the Cure Glomerulonephropathy Network (CureGN)
Bibliographic record
Abstract
Introduction The 2012 Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guidelines for Glomerulonephritis recommend that patients with membranous nephropathy (MN) at risk for progression receive immunosuppressive therapy (IST), usually after 6 months of observation. A cyclophosphamide (CYC) or calcineurin inhibitor (CNI)–based regimen is recommended as first-line IST. However, the extent to which KDIGO recommendations are adopted in practice remains largely unknown. Methods We evaluated prescribing practice among patients with primary MN (diagnosed 2010–2018) enrolled in the Cure Glomerulonephropathy Network (CureGN) cohort study. We also evaluated the availability of testing for phospholipase A2 receptor (PLA2R) in the contemporary era. Results Among 361 patients (324 adults and 37 children) with MN who were IST-naïve at biopsy and had at least 6 months of follow-up, 55% of adults and 58% of children initiated IST <6 months after biopsy. Of these, 1 in 5 had no indication for (i.e., urine protein-to-creatinine ratio [uPCR] <4 g/g) or an apparent contraindication to (i.e., an estimated glomerular filtration rate [eGFR] <30 ml/min per 1.73 m 2 ) IST. As first-line IST, half of treated patients received either CYC (16% of adults; 0% of children) or a CNI (40% and 46%, respectively), whereas 1 in 5 received corticosteroid monotherapy (20% and 27%, respectively) and 1 in 6 rituximab (15% and 15%, respectively). More than 80% of surveyed centers had access to PLA2R testing. Conclusion These findings suggest that providers are not aware of, or lack confidence in, current KDIGO guidelines for MN. Treatment patterns observed in this cohort might critically inform the drafting of planned updates to KDIGO guidelines.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".