Body composition parameters in systemic sclerosis—a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The aim of this systematic review and meta-analysis was to summarize current evidence regarding body composition (BC) in SSc in order to gain new insights and improve clinical care in the context of the nutritional status of SSc patients. METHODS: The databases Web of Science, PubMed, Scopus and Cochrane Library were searched on 4 January 2023. Studies were included if they provided data regarding BC obtained by dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) in patients with SSc and healthy controls (HC). The study design criteria for inclusion were cohort and observational studies. The risk of bias assessment was performed using the Newcastle-Ottawa scale. For meta-analysis, mean difference with a 95% confidence interval was obtained and all results were depicted as forest plots. RESULTS: The number of retrieved publications was 593, of which nine were included in a random-effects meta-analysis totalling 489 SSc patients and 404 HC. Overall, significantly lower body mass index, lean mass (LM), fat mass (FM) and phase angle values were found in SSc patients when compared with HC. Furthermore, FM and LM were significantly lower in SSc patients when the DXA method was applied, whereas the same parameters were comparable between two groups of participants when BIA was applied. CONCLUSION: Altered BC is characteristic of SSc patients indicating the need for regular nutritional status assessment in order to improve the quality of life and clinical care of patients with SSc.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".