Factors associated with physical function among people with systemic sclerosis: a SPIN cohort cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: To compare physical function in systemic sclerosis (SSc, scleroderma) to general population normative data and identify associated factors. METHODS: Scleroderma Patient-centered Intervention Network Cohort participants completed the Physical Function domain of the Patient-Reported Outcomes Measurement Information System Version 2 upon enrolment. Multivariable linear regression was used to assess associations of sociodemographic, lifestyle, and disease-related variables. RESULTS: Among 2385 participants, the mean physical function T-score (43.7, SD = 8.9) was ∼2/3 of a standard deviation (SD) below the US general population (mean = 50, SD = 10). Factors associated in the multivariable analysis included older age (-0.74 points per SD years, 95% CI -0.78 to -1.08), female sex (-1.35, -2.37 to -0.34), fewer years of education (-0.41 points per SD in years, -0.75 to -0.07), being single, divorced, or widowed (-0.76, -1.48 to -0.03), smoking (-3.14, -4.42 to -1.85), alcohol consumption (0.79 points per SD drinks per week, 0.45-1.14), BMI (-1.41 points per SD, -1.75 to -1.07), diffuse subtype (-1.43, -2.23 to -0.62), gastrointestinal involvement (-2.58, -3.53 to -1.62), digital ulcers (-1.96, -2.94 to -0.98), moderate (-1.94, -2.94 to -0.93) and severe (-1.76, -3.24 to -0.28) small joint contractures, moderate (-2.10, -3.44 to -0.76) and severe (-2.54, -4.64 to -0.44) large joint contractures, interstitial lung disease (-1.52, -2.27 to -0.77), pulmonary arterial hypertension (-3.72, -4.91 to -2.52), rheumatoid arthritis (-2.10, -3.64 to -0.56) and idiopathic inflammatory myositis (-2.10, -3.63 to -0.56). CONCLUSION: Physical function is impaired for many individuals with SSc and is associated with multiple disease factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".