Immunosuppressive Therapies for the Induction Treatment of Proliferative Lupus Nephritis: A Systematic Review and Network Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate and determine the most effective immunosuppressive therapy for the induction treatment of proliferative lupus nephritis (PLN) based on renal remission. METHODS: A systematic review of randomized controlled trials was conducted. The outcomes were renal remission at 6 months: (1) normalization of serum creatinine [(sCr), or within 15% of the normal range, i.e., sCr < 132 µmol/l - creatinine remission]; and (2) proteinuric remission (prU < 0.5 g/day/1.73m(2)). A Bayesian network metaanalysis was used. RESULTS: The OR (95% credible interval) of inducing an sCr remission at 6 months was 1.70 (0.51, 6.87) for mycophenolate mofetil (MMF) versus cyclophosphamide (CYC); 2.16 (0.38, 13.36) for tacrolimus (Tac) versus CYC; and 1.25 (0.13, 10.51) for Tac versus MMF. For proteinuric remission the OR was 1.46 (0.81, 3.04) for MMF versus CYC; 1.96 (0.80, 5.11) for Tac versus CYC; and 1.34 (0.43, 3.90) for Tac versus MMF. The probability (95% credible interval) of inducing a creatinine remission at 6 months was Tac 56% (19%, 88%); MMF 51% (23%, 79%); and CYC 37% (28%, 47%). The probability of inducing a proteinuric remission was Tac 41% (23%, 63%); MMF 34% (23%, 50%); CYC 26% (20%, 32%); azathioprine 10% (1%, 55%); prednisone 11% (2%, 38%). None of the results were conclusive when examined in a sensitivity analysis. CONCLUSION: There is currently insufficient evidence to determine which of these immunosuppressive agents is superior. The probability of renal remission is 50% or lower at 6 months.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".