Rituximab Treatment in Adult Patients With Idiopathic Inflammatory Myositis
Bibliographic record
Abstract
OBJECTIVE: This systematic review and meta-analysis assess the efficacy and safety of rituximab (RTX) in treating idiopathic inflammatory myositis (IIM). METHODS: PubMed and Embase were systematically searched for trials and observational studies involving RTX use in IIM. Data were analyzed using a random-effects model to generate pooled estimates for overall response, complete remission, partial response, and adverse events, with subgroup analyses by myositis type and RTX dosage (PROSPERO registered number CRD42022353740). Risk of bias assessments were done using the Newcastle-Ottawa Scale for observational studies and risk of bias 1 tool for trials. RESULTS: Seventeen studies (1 randomized controlled trial and 16 observational studies), encompassing 362 patients, were included. The overall pooled response rate was 70% (95% confidence interval [CI]: 57%-82%; I2 = 74%, p < 0.001). Complete remission occurred in 13% (95% CI: 3%-25%; I2 = 79%, p < 0.001) and partial response in 48% (95% CI: 30%-67%; I2 = 87%, p < 0.001), both with significant heterogeneity. Subgroup analysis revealed high response rates across all myositis types: polymyositis 69%, dermatomyositis 67%, antisynthetase syndrome 70%, juvenile dermatomyositis 60%, and immune-mediated necrotizing myopathy 86%. Response rates were similar between RTX induction doses of 1 g IV on days 0 and 14 (68%) and 375 mg/m 2 weekly for 4 weeks (71%). Reported adverse events totaled 120, including infusion reactions (18.5%) and infections (12.4%). CONCLUSIONS: RTX shows a favorable clinical response in IIM treatment, though response rates vary. There was a significant heterogeneity in treatment effect estimates that are based on a small number of patients. The incidence of infusion reactions and infections highlights the need for careful monitoring. Further controlled trials are essential to refine treatment protocols and evaluate long-term outcomes for RTX's role in IIM.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".