Biologic Therapy for Systemic Sclerosis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Biologic agents are increasingly used in the rheumatic diseases. Their role in patients with systemic sclerosis (SSc) is uncertain. Our aim was to evaluate the effectiveness and safety of biologic agents in SSc. We review the evidence for the use of biologic agents to improve inflammatory arthritis, disability, and skin score, and we review adverse effects with biologic agents in patients with SSc. METHODS: A systematic literature review was performed to identify studies evaluating the use of biologic agents in SSc. Medline, Embase, CINAHL, and Cochrane Database of Systematic Reviews were searched. A standardized abstraction form was used to extract biologic agent, study design, sample size, treatment effect, and adverse effects. RESULTS: A total of 23 studies from 1413 citations were evaluated. Three studies evaluated infliximab, 3 evaluated etanercept, 3 evaluated antithymocyte globulin, 3 evaluated imatinib, 6 evaluated rituximab, and 1 study each evaluated interferon-γ (IFN-γ), IFN-α, relaxin, delipidated, deglycolipidated Mycobacterium vaccae, human anti-transforming growth factor ß1 antibody, and oral type I collagen. Studies of etanercept and infliximab suggest improvements in inflammatory arthritis and Health Assessment Questionnaire Disability Index (HAQ-DI). None of the other biologic agents demonstrated reproducible, statistically significant improvements in joint count, HAQ-DI, or skin score. CONCLUSION: Anti-tumor necrosis factor-α agents may improve inflammatory arthritis and disability in SSc. The effect on skin score is uncertain. Adequately powered trials are needed to evaluate efficacy, and longitudinal studies are needed to evaluate longterm safety of these agents in SSc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".