Anti-CD20 Antibodies for Idiopathic Nephrotic Syndrome in Children
Bibliographic record
Abstract
Rituximab, a chimeric anti-CD20 monoclonal antibody originally licensed for lymphoma, is emerging as a novel steroid-sparing agent for idiopathic nephrotic syndrome in children. The potential use of anti-CD20 monoclonal antibodies in idiopathic nephrotic syndrome has contributed to shifting the view of podocytopathies from T cell-mediated to more complex immunomediated disorders that can benefit from targeting B cells and other mediators of the early immune response. Clinical data on the use of rituximab also have implications on disease management and classification. In this review, we present results of clinical studies that support rituximab as an effective steroid-sparing agent in steroid-dependent idiopathic nephrotic syndrome. Recent randomized controlled trials suggest that potential benefits of rituximab therapy in steroid-dependent forms of idiopathic nephrotic syndrome vary depending on whether children are dependent on steroids alone or on both steroids and calcineurin inhibitors, with greater probabilities to achieve drug-free remission in the former group. Multiple-drug dependence may identify a different disease state with different prognosis and treatment options. Insufficient data are available on optimal use of rituximab as a maintenance steroid-sparing agent in these steroid-sensitive forms of the disease, including how often and for how long rituximab infusions should be repeated to maximize expected benefits and minimize potential harms. Finally, one randomized controlled trial in children with steroid-resistant idiopathic nephrotic syndrome yielded negative results. New anti-CD20 antibodies are under study in this patient population.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".