Work Disability in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a multisystem disease associated with significant morbidity and increased mortality. Little is known about work disability in SSc. We undertook this study to determine the prevalence and demographic and clinical correlates of work disability in a large cohort of patients with SSc. METHODS: Cross-sectional, multicenter study of patients from the Canadian Scleroderma Research Group Registry. Patients were assessed with detailed clinical histories, medical examinations, and self-administered questionnaires. The primary outcome was self-reported work disability. Multiple logistic regression was used to assess the relationship between selected demographic and clinical variables and work disability. RESULTS: Of the 643 patients available for this study, 133 (21%) reported that they were work disabled. Work disability in SSc was common, even in people with short disease duration, and increased steadily with increasing disease duration: among those who were <or= 65 years and who reported being either disabled or working, 28.0% and 44.8% of patients with disease duration of < 2 and 10-15 years, respectively, reported that they were work-disabled. The significant correlates of work disability included co-morbidities, disease duration, diffuse disease, disease severity, pain, fatigue, and physical function. CONCLUSION: Work disability is prevalent, occurs early, and is associated with markers of disease severity and functional status. Further research is needed to identify other, potentially modifiable, risk factors for work disability in 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".