Use of Rituximab in Childhood Idiopathic Nephrotic Syndrome
Bibliographic record
Abstract
Rituximab is an established therapy in children with idiopathic nephrotic syndrome to sustain short- to medium-term disease remission and avoid steroid toxicities. Recent trials focus on its use as a first-line agent among those with milder disease severity. Rituximab is used in multidrug refractory nephrotic syndrome and post-transplant disease recurrence, although the evidence is much less substantial. Available data suggest that the treatment response to rituximab depends on various patient factors, dosing regimen, and the concomitant use of maintenance immunosuppression. After repeated treatments, patients are found to have an improving response overall with a longer relapse-free period. The drug effect, however, is not permanent, and 80% of patients eventually relapse and many will require an additional course of rituximab. This underpins the importance of understanding the long-term safety profile on repeated treatments. Although rituximab appears to be generally safe, there are concerns about long-term hypogammaglobulinemia, especially in young children. Reliable immunophenotyping and biomarkers are yet to be discovered to predict treatment success, risk of both rare and severe side effects, e.g. , persistent hypogammaglobulinemia, and guiding of redosing strategy. In this review, we highlight recent advances in the use of rituximab for childhood nephrotic syndrome and how the therapeutic landscape is evolving.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".