Adverse Events Following Rituximab Infusion in Children with Nephrotic Syndrome: A Systematic Review
Bibliographic record
Abstract
Background: Rituximab (RTX) is often used off-label in children with various kidney diseases. However, there are limited data on the frequency and severity of adverse events and side effects (AE/SE) observed in children following RTX administration. The aim of this systematic review is to evaluate the AE/SE of RTX in children with nephrotic syndrome (NS). Methods: Six databases were searched to include literature from 1991-2019 that provided AE/SE data on children (≤18 yrs) receiving RTX. Article screening, data extraction, and quality assessment were independently completed and verified by two reviewers. Primary outcome was the cumulative incidence of AE/SE. Secondary outcomes included the severity (evaluated by the Common Terminology Criteria for Adverse Events), timing, and affected body systems of each AE/SE. Results: Out of 3364 citations, 40 articles were included and 13% were randomized controlled trials (Table). Most reported AE/SE were infusion-related reactions (22.0%), infections (13.9%), granulocytopenia (3.9%), and hypogammaglobulinemia (2.7%). Reporting of the timing or duration of AE/SE was heterogenous and frequently incomplete. Out of all patients experiencing AE/SE (n=455), 12.7% were severe (grade 3-5), 50.8% were mild (grade 1-2), and the severity in the rest were indeterminable. Overall, 53/1143 (4.6%) children experienced severe AE/SE. Conclusions: The majority of children receiving RTX for NS do not experience serious AE/SE and RTX is generally well-tolerated. However, standardized reporting of AE/SE including timing, duration, and severity grade is warranted in future studies.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".