Immunosuppressive agents for frequently relapsing/steroid-dependent nephrotic syndrome in children: a systematic review and network meta-analysis
Bibliographic record
Abstract
Aim: This study aimed to systematically compare the efficacy of various immunosuppressive agents in treating pediatric frequently relapsing or steroid-dependent nephrotic syndrome (FRSDNS). Methods: We conducted systematic searches of PubMed, Embase, the Cochrane Library, and the Web of Science up to May 23, 2023. Outcome measures included relapses within 1 year, mean cumulative exposure to corticosteroids, patients with treatment failure at 1 year, relapse-free survival during 1 year, and adverse events. The quality of the included studies was evaluated using the modified Jadad scale, the Methodological Index for Non-Randomized Studies (MINORS), and the modified Newcastle-Ottawa Scale (NOS). Results: Rituximab was found to be the most likely (92.44%) to be associated with the fewest relapses within 1 year and was also most likely (99.99%) to result in the lowest mean cumulative exposure to corticosteroids. Rituximab had the highest likelihood (45.98%) of being associated with the smallest number of patients experiencing treatment failure at 1 year. CsA was most likely (57.93%) to achieve the highest relapse-free survival during 1 year, followed by tacrolimus (26.47%) and rituximab (30.48%). Rituximab showed no association with serious side effects and had comparable adverse effects to ofatumumab and tacrolimus. Conclusion: Rituximab may be the most favorable immunosuppressive agent for treating pediatric FRSDNS. Nephrologists should consider this drug, along with their clinical experience, patient characteristics, and cost considerations, when choosing a treatment approach.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".