Comparative Effectiveness of Cyclophosphamide and Calcineurin Inhibitors in Childhood Nephrotic Syndrome
Bibliographic record
Abstract
Background: Nephrotic syndrome is a common pediatric kidney disease with high morbidity. Cyclophosphamide and calcineurin inhibitors are the most used second-line medications, but their comparative effectiveness is unknown. Methods: Using target trial methods, we emulated a pragmatic, open-label clinical trial using Insight into Nephrotic Syndrome: Investigating Genes, Health, and Therapeutics study data. We included children (1-18yr) diagnosed with nephrotic syndrome from 1996-2019 in Toronto, Canada that initiated cyclophosphamide or calcineurin inhibitors. Randomization was emulated by propensity score overlap weighting. The primary outcome was time-to-relapse, analyzed by weighted Cox proportional hazards models. Results: Of 578 children, 252 started cyclophosphamide and 131 calcineurin inhibitors. Baseline characteristics were balanced after propensity score weighting. During median 5.5-year (IQR 2.5-9.2) follow-up, there was no difference in relapses after calcineurin inhibitor vs. cyclophosphamide (HR 1.25, 95%CI 0.84-1.87). There were also no differences in relapses by 1, 2, or 5-years, hypertension, or chronic kidney disease. Calcineurin inhibitor use was associated with hospitalization (HR 1.83, 95%CI 1.14-2.92) and intravenous albumin use (HR 2.81, 95%CI 1.65-4.81). Conclusion: There was no difference in risk of relapse after cyclophosphamide vs. calcineurin inhibitor treatment in childhood nephrotic syndrome. Cyclophosphamide treatment is shorter in duration and more accessible in low-to-middle income countries. Funding: Government Support – Non-U.S.Figure. Weighted relapse-free survival after cyclophosphamide vs. calcineurin inhibitor initiation
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".