Mycophenolate Mofetil for Induction Treatment of Lupus Nephritis: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: to systematically review the efficacy and safety of mycophenolic acid and mycophenolate mofetil (MMF) compared to cyclophosphamide (CYC) for the induction treatment of lupus nephritis (LN). METHODS: medline, Embase, the Cochrane Center Register of Controlled Trials, and abstracts presented in major international conferences were searched for randomized controlled trials. The primary outcome was renal remission (complete, partial, and overall) and secondary outcomes were adverse events during study period and longterm followup data. Data were compared between groups and relative risk (RR) and 95% CI were calculated. RESULTS: four trials of a total of 618 patients were included. MMF was not superior to CYC for renal remission (partial RR 0.94, 95% CI 0.80 to 1.12; complete RR 0.67, 95% CI 0.35 to 1.28, and overall RR 0.89, 95% CI 0.71 to 1.10). There was a significant reduction in alopecia (RR 5.77, 95% CI 1.56 to 21.38) and amenorrhea (RR 6.64, 95% CI 2.00 to 22.07) with the use of MMF compared to CYC. These results should be interpreted with caution given the width of the CI. There was no significant difference for infections, leukopenia, gastrointestinal symptoms, herpes zoster, endstage renal disease, and death among groups during study period and longterm followup data. CONCLUSION: we could not show that MMF is superior to CYC for the induction treatment of LN. Patients treated with MMF showed reduced risk of certain side effects. MMF can be used as an alternative to CYC for the induction treatment of LN.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.026 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".