Work Disability in Scleroderma is Greater than in Rheumatoid Arthritis and is Predicted by High HAQ Scores
Bibliographic record
Abstract
OBJECTIVES: To estimate the frequency of work disability (WD) in a cohort of patients with Systemic Sclerosis (SSc) vs an internal control group of patients with rheumatoid arthritis (RA) with a known high frequency of WD; and to investigate the association between WD and other factors including Health Assessment Questionnaire Disability Index (HAQ-DI) scores, HAQ pain, age, sex, disease duration and education level. METHODS: Cross-sectional data on WD status were obtained from a questionnaire sent to all SSc (n = 35 limited [lcSSc], 26 diffuse [dcSSc]) and a subset of RA patients (n=104) from a rheumatology practice. WD data, HAQ-DI scores, and demographic/clinical features (age, sex, high school education, disease duration and SSc disease subtype [dcSSc vs lcSSc]) were recorded. RESULTS: The proportion with WD was 0.56 in SSc (95% CI: 0.43-0.68) vs 0.35 in RA (95% CI: 0.25-0.44), p= 0.009. HAQ-DI scores were significantly higher in work-disabled SSc and RA patients vs those who were employed (p=0.0001, and p <0.0001). Multivariate logistic regression analysis demonstrated that higher HAQ-DI scores (β=1.78, p <0.001), disease type (dcSSc, lcSSc, RA) (β=1.32 for dcSSc, p=0.032), and self-reported disease duration (β=0.04, p=0.042) were significantly associated with WD (R(2)=0.311). Adding a work-related factor (self-reported physically demanding work) improved the regression model (R(2)=0.346) and strengthened the HAQ-DI (β=1.86, p <0.001) and lcSSc (β=1.24, p=0.024) coefficients. CONCLUSION: The frequency of WD in SSc was high and was greater than in RA. SSc (and dcSSc) had significantly more WD than RA. The HAQ-DI was strongly associated with WD in SSc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".