Work Disability in Early Systemic Sclerosis: A Longitudinal Population-based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To study work disability (WD) with reference to levels of sick leave and disability pension in early systemic sclerosis (SSc). METHODS: Patients with SSc living in the southern part of Sweden with onset of their first non-Raynaud symptom between 2003 and 2009 and with a followup of 36 months were included in a longitudinal study. Thirty-two patients (26 women, 24 with limited SSc) with a median age of 47.5 years (interquartile range 43-53) were identified. WD was calculated in 30-day intervals from 12 months prior to disease onset until 36 months after, presented as the prevalence of WD per year (0-3) and as the period prevalence of mean net days per month (± SD). Comparisons were made between patients with different disease severity and sociodemographic characteristics, and between patients and a reference group (RG) from the general population. RESULTS: Seventy-eight percent had no WD 1 year prior to disease onset, which decreased to 47% after 3 years. The relative risk for WD in patients with SSc compared with RG was 0.95 (95% CI 0.39-2.33) at diagnosis, and increased to 2.41 (1.28-4.55) after 3 years. There were no significant correlations between WD and disease severity, but between WD and years at workplace (rs = -0.72; p = 0.002), education (rs = -0.51; p = 0.004), and sickness absence the month before disease onset (rs = 0.58; p = 0.001), respectively. CONCLUSION: Considerable increase in WD was noted 3 years after disease onset. Limited education, fewer years at workplace, and sickness absence before disease onset may be risk factors for sustained WD.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".