Risk Factors for Disease Progression for Adults and Children With Membranous Nephropathy in the Cure Glomerulonephropathy Network (CureGN)
Bibliographic record
Abstract
Introduction: Primary membranous nephropathy (pMN) is a frequent cause of nephrotic syndrome in adult patients without diabetes. Recognizing the major shift in the classification of pMN based on target antigen and the management of patients with pMN with more widespread use of rituximab (RTX), we sought to better characterize the clinical course and risk factors in adults and children with pMN. Methods: We used the Cure Glomerulonephropathy (CureGN) prospective cohort of patients with pMN diagnosed using biopsy between 2010 and 2023. We report time to kidney outcomes using adjusted Cox proportional hazards models. Results: In total, 591 patients (537 adults and 54 children) were evaluated with 9% reaching kidney failure. Anti-B cell therapy was used in 44% of patients. Age < 18 years, self-reported Black/African American race, proteinuria > 3 g/g, and lower estimated glomerular filtration rate (eGFR) at enrollment were associated with worse kidney survival. Black race (adjusted hazard ratio [HR]: 1.8; 95% confidence interval [CI]: 1.1-2.9) and age < 18 years (adjusted HR: 3.7; 95% CI: 2.0-7.1) were associated with an increased risk of kidney failure and/or > 40% eGFR decline after adjusting for exposure to immunosuppression. Latinx ethnicity was associated with a lower likelihood of reaching complete proteinuria remission (adjusted HR: 0.4; 95% CI: 0.2-0.7). Conclusion: This study unveils self-reported Black race, young age (aged < 18 years) and Latinx ethnicity as potential risk factors associated with worse kidney outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".