Characteristics of Joint Involvement and Relationships with Systemic Inflammation in Systemic Sclerosis: Results from the EULAR Scleroderma Trial and Research Group (EUSTAR) Database
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of and independent factors associated with joint involvement in a large population of patients with systemic sclerosis (SSc). METHODS: This study was cross-sectional, based on data collected on patients included in the European League Against Rheumatism (EULAR) Scleroderma Trials and Research (EUSTAR) registry. We queried this database to extract data regarding global evaluation of patients with SSc and the presence of any clinical articular involvement: synovitis (tender and swollen joints), tendon friction rubs (rubbing sensation detected as the tendon was moved), and joint contracture (stiffness of the joints that decreased their range of motion). Overall joint involvement was defined by the occurrence of synovitis and/or joint contracture and/or tendon friction rubs. RESULTS: We recruited 7286 patients with SSc; their mean age was 56 +/- 14 years, disease duration 10 +/- 9 years, and 4210 (58%) had a limited cutaneous disease subset. Frequencies of synovitis, tendon friction rubs, and joint contractures were 16%, 11%, and 31%, respectively. Synovitis, tendon friction rubs, and joint contracture were more prevalent in patients with the diffuse cutaneous subset and were associated together and with severe vascular, muscular, renal, and interstitial lung involvement. Moreover, synovitis had the highest strength of association with elevated acute-phase reactants taken as the dependent variable. CONCLUSION: Our results highlight the striking level of articular involvement in SSc, as evaluated by systematic examination in a large cohort of patients with SSc. Our data also show that synovitis, joint contracture, and tendon friction rubs are associated with a more severe disease and with systemic inflammation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".