Long-Term Comparative Effectiveness of Rituximab vs. Calcineurin Inhibitors for the Treatment of Membranous Nephropathy
Bibliographic record
Abstract
Background: Clinical trials in membranous nephropathy (MN) may establish treatment efficacy for short-term outcomes but are limited by small sample sizes and short study duration. This study applied modern statistical methods to CureGN data to compare effectiveness of rituximab (RTX) and calcineurin inhibitors (CNIs) for long-term MN outcomes. Methods: MN participants initiating RTX or CNI 6+ months after any previous immunosuppressant (IST) exposure were eligible. Propensity scores (PS) based on demographics, labs, pathology, and comorbidities at treatment start were generated and PS-matching with exact matching on history of RTX and CNI was applied. Individuals were censored if they started a different IST during follow-up and inverse probability weights were used. Outcomes included time from treatment start to proteinuria remission, relapse following remission, and kidney disease progression. Hazard ratios (HR) were estimated from Cox models with variance adjustment to account for matching and multiple treatments within individuals. Differences in restricted mean survival time (RMST) were also estimated. Results: 331 treatment initiations across 254 unique participants were eligible, with median follow-up 53 months (IQR=26, 79). Participants on CNIs had significantly higher risks of disease progression (HR=2.83; 95% CI: 1.20, 6.68) than RTX. Proteinuria remission risks were similar in both groups (HR=0.97; 95% CI: 0.61, 1.52), though CNIs had higher risks of relapse (HR=2.14, 95% CI: 1.04, 4.40). Conclusion: RTX was more effective for preserving eGFR than CNIs. Proteinuria remission and relapse results align with a prior clinical trial but extend trial results past 24 months. High-quality evidence on long-term treatment effectiveness in rare diseases can be generated from observational studies.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".