Treatments for Lupus Nephritis: A Systematic Review and Network Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: To compare benefits and harms of lupus nephritis (LN) induction and maintenance treatments. METHODS: We performed a systematic review and Bayesian network metaanalyses of randomized controlled trials (RCT) of immunosuppressive drugs or corticosteroids (CS) in LN. OR and 95% credible intervals (CrI) were calculated. RESULTS: There were 65 RCT that met inclusion and exclusion criteria. Significantly lower risk of endstage renal disease (ESRD; 17 studies) was seen with cyclophosphamide (CYC; OR 0.49, 95% CrI 0.25-0.92) or CYC + azathioprine (AZA; OR 0.18, 95% CrI 0.05-0.57) compared with standard-dose CS, and with high-dose (HD) CYC (OR 0.16, 95% CrI 0.03-0.61) or CYC + AZA (OR 0.10, 95% CrI 0.03-0.34) compared with HD CS. HD CS was associated with higher risk of ESRD compared with CYC (OR 3.59, 95% CrI 1.30-9.86), AZA (OR 2.93, 95% CrI 1.08-8.10), or mycophenolate mofetil (MMF; OR 7.05, 95% CrI 1.66-31.91). Compared with CS, a significantly higher proportion of patients had renal response (14 studies) when treated with CYC (OR 1.98, 95% CrI 1.13-3.52), MMF (OR 2.42, 95% CrI 1.27-4.74), or tacrolimus (TAC; OR 4.20, 95% CrI 1.29-13.68). No differences were noted for the risk of malignancy (15 studies). The risk of herpes zoster (17 studies) was as follows: OR (95% CrI) MMF versus CS 4.38 (1.02-23.87), CYC versus CS 6.64 (1.97-25.71), TAC versus CS 9.11 (1.13-70.99), and CYC + AZA versus CS 8.46 (1.99-43.61). CONCLUSION: Renal benefits and the risk of herpes zoster were higher for immunosuppressive drugs versus CS. Data on relative and absolute differences are now available, which can be incorporated into patient-physician discussions related to systemic lupus erythematosus medication use.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".