Comparative Effectiveness of Immunosuppressive Medications in Nephrotic Syndrome: A Cure Glomerulonephropathy (CureGN) Study Report
Bibliographic record
Abstract
Background: Rituximab is increasingly used to treat frequently-relapsing nephrotic syndrome in children and young adults. Yet, the real-world comparative effectiveness of immunosuppressive medications is unclear. Methods: Using target trial methods, we emulated a multi-center, open-label, pragmatic randomized controlled trial with CureGN data. We included children and young adults (<40 years) diagnosed with minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS) that had prior complete remission and initiated rituximab, mycophenolate mofetil (MMF), or calcineurin inhibitors (CNI). Randomization was emulated by propensity score overlap weighting. The primary outcome was time-to-relapse using weighted Cox proportional hazards models. Results: Of 221 eligible CureGN participants, 111 initiated rituximab, 46 MMF, and 64 CNI. Baseline characteristics were balanced after propensity score weighting. Mean age was 13.3 years and 80% had prior steroid-sparing drug use. During median 4.3-year (IQR 1.9-6.1) follow-up, relapse occurred in 56%, 46%, and 58% after rituximab, MMF, and CNI use, respectively. There was no difference in relapses after rituximab vs. MMF or CNI (weighted HR 1.16, 95%CI 0.74-1.81). There were also no differences in relapse rates, kidney function decline, or adverse events. Conclusion: There was no difference in relapse risk after rituximab vs. MMF or CNI among children and young adults with difficult-to-treat nephrotic syndrome. Therapeutic selection should be a shared decision, considering medication-specific side-effects, costs, access, duration, and patient adherence. Funding: Government Support – Non-U.S.Figure. Weighted relapse-free survival after rituximab vs. MMF or CNI use
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".