The association of sociodemographic and disease variables with hand function: a Scleroderma Patient-centered Intervention Network cohort study.
Bibliographic record
Abstract
OBJECTIVES: Impaired hand function in systemic sclerosis (SSc) is a primary cause of disability and contributes diminished health-related quality of life. The objective of the present study was to evaluate sociodemographic, lifestyle, and disease-related factors independently associated with hand function in SSc. METHODS: Patients enrolled in the Scleroderma Patient-centered Intervention Network Cohort who completed baseline study questionnaires between March 2014 and September 2017 were included. Hand function was measured using the Cochin Hand Function Scale (CHFS). Multiple linear regression analysis was used to identify independent correlates of impaired hand function. RESULTS: Among 1193 participants (88% female), the mean CHFS score was 13.3 (SD=16.1). Female sex (standardised regression coefficient, beta (β)=.05), current smoking (β=.07), higher BMI (β=.06), diffuse SSc (β=0.14), more severe Raynaud's scores (β=.23), more severe finger ulcer scores (β=.23), moderate (β=0.19) or severe small joint contractures (β=.20), rheumatoid arthritis (β=0.07), and idiopathic inflammatory myositis (β=0.06) were significantly associated with higher CHFS scores (more impaired hand function). Consumption of 1-7 alcoholic drinks per week (β=-0.07) was associated with lower CHFS scores (less impaired hand function) compared to no drinking. CONCLUSIONS: Multiple factors are associated with hand function in SSc. The presence of moderate or severe small joint contractures, the presence of digital ulcers, and severity of Raynaud's phenomenon had the largest associations. Effective interventions are needed to improve the management of hand function in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".