Sarcopenia in systemic sclerosis: prevalence and impact—a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This review aims to provide an estimate of sarcopenia prevalence and its impact on clinical characteristics in patients with systemic sclerosis (SSc). DESIGN: Systematic review and meta-analysis. DATA SOURCES: Embase, Medline, Web of Science and the Cochrane Central Register of Controlled Trials were systemically searched from inception to 24 May 2023. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included observational studies that reported the prevalence of sarcopenia in patients with SSc. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently performed study selection and data extraction using standardised methods. Risk of bias was assessed using the Agency for Healthcare Research and Quality Scale and the Newcastle-Ottawa Scale. Meta-analysis was conducted using random effects models. RESULTS: A total of 4583 articles were screened and 9 studies with data from 815 patients were included in the analysis (8 cross-sectional studies and 1 retrospective cohort study). The overall prevalence of sarcopenia in patients with SSc was 22% (95% CI 17% to 28%). Patients with SSc with sarcopenia had a poorer quality of life (mean difference -12.02; 95% CI -19.11 to -4.93) and higher C reactive protein (CRP) levels (standardised mean difference 0.67; 95% CI 0.35 to 1.00). CONCLUSIONS: Sarcopenia is common in patients with SSc. Patients with SSc with sarcopenia had a worse quality of life and higher CRP levels, based on our findings. Given the detrimental impact of sarcopenia on quality of life, future efforts aimed at early identification of sarcopenia in the clinical assessment of patients with SSc may have significance. PROSPERO REGISTRATION NUMBER: CRD42022368326.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".