Immunosuppressive Therapies for the Maintenance Treatment of Proliferative Lupus Nephritis: A Systematic Review and Network Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: To determine the most effective immunosuppressive therapy for the longterm management of proliferative lupus nephritis (PLN) based on the outcome of renal failure. METHODS: A systematic review of randomized controlled trials (RCT) was conducted. MEDLINE and EMBASE were searched. RCT designed to examine the maintenance treatment effectiveness of immunosuppressive agents for PLN were included. A Bayesian network metaanalysis of 2-arm and 3-arm trials was used. A skeptical prior assumption was used in sensitivity analysis. Four immunosuppressive agents were evaluated: cyclophosphamide (CYC), azathioprine (AZA), mycophenolate mofetil (MMF), and prednisone alone. The outcome of interest was renal failure during the study period, defined by serum creatinine (sCr) > 256 µmol/l, doubling of sCr from baseline, and/or endstage renal disease. RESULTS: The OR (95% credible interval) of developing renal failure at 2-3 years was 0.72 (0.11, 4.49) for AZA versus CYC, 0.32 (0.04, 2.25) for MMF versus CYC, 2.40 (0.22, 36.94) for prednisone alone versus CYC, and 0.45 (0.11, 1.48) for MMF versus AZA. The probability (95% credible interval) of developing renal failure at 2 years as expected for each agent was 6% (0.7%, 24%) for MMF, 12% (2%, 37%) for AZA, 16% (5%, 33%) for CYC, and 31% (5%, 81%) for prednisone alone. After applying a skeptical prior in the Bayesian analysis, there was no evidence of benefit for 1 therapy over another. CONCLUSION: Although the data suggest that MMF may be superior to other treatments for the maintenance treatment of PLN, the evidence is not conclusive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".