Prevalence of rheumatoid cachexia in rheumatoid arthritis: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Low muscle mass occurs in patients with rheumatoid arthritis without weight loss; this condition is referred as rheumatoid cachexia. The aim of the current study was to perform a systematic review with meta-analysis to determine the rheumatoid cachexia prevalence. METHODS: A systematic review with meta-analysis of observational studies published in English, between 1994 and 2016, was conducted using MEDLINE (via PubMed) and other relevant sources. Search strategies were based on pre-defined keywords and medical subject headings. The methodological quality of included studies was assessed using the Newcastle-Ottawa Scale. Meta-analysis was used to estimate the prevalence, and because studies reported different methods and criteria to estimate body composition and prevalence of rheumatoid cachexia, subgroup analyses were performed. Meta-regression adjusted for the 28-joint disease activity score and disease duration (years) was performed (significance level at P ≤ 0.05). RESULTS: Of 136 full articles (one duplicate publication) screened for inclusion in the study, eight were included. The estimated overall prevalence of rheumatoid cachexia was 19% [95% confidence interval (CI) 07-33%]. This prevalence was 29% (95% CI 15-46%) when body composition was measured by dual-energy X-ray absorptiometry. When the diagnostic criteria were fat-free mass index below the 10th percentile and fat mass index above the 25th percentile, rheumatoid cachexia prevalence was 32% (95% CI 14-52%). The 28-joint disease activity score and disease duration had no influence on the estimated prevalence of rheumatoid cachexia (P > 0.05). Most studies were rated as having moderate methodological quality. CONCLUSIONS: Meta-analysis showed a prevalence of rheumatoid cachexia of 15-32%, according to different criteria, demonstrating that this condition is a frequent comorbidity of rheumatoid arthritis. To better understand its clinical impact, more studies using standardized definitions and prospective evaluations are urgently needed.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".