Work disability in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence, accrual over time, and risk factors of work disability in patients with systemic lupus erythematosus (SLE). METHODS: We studied 432 patients from an inception cohort. Work disability was measured from a single self-report question. Data were prospectively collected and included sociodemographic information, clinical lupus features including activity (Systemic Lupus Erythematosus Disease Activity Index 2000 update [SLEDAI-2K]), damage (Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index), and organ involvement, as well as health status (Short Form 36 [SF-36]), comorbidity, and medication use. Student's t-test and Wilcoxon's rank sum test were used to compare continuous variables and chi-square tests were used for dichotomous variables. Descriptive survival curves of time to work disability were presented. Bivariate and multivariate logistic regressions were used to describe the relationships between clinically relevant factors and work disability. RESULTS: Of 432 patients, 88% were women and 73% were white. Within the first year of diagnosis, 47% of patients were employed, 7% had a disability, and 7% were on sick leave. Overall, work disability was found in 98 (23%) patients. Risk factors for work disability found in the multivariate regression analysis were younger age at diagnosis, less education, fibromyalgia, hypertension, higher first-visit SLEDAI-2K score, and lower first-visit SF-36 score. CONCLUSION: Work disability is frequent in patients with SLE, with a cumulative prevalence of 23%. Work disability was associated with a complex array of health factors, including comorbidity, physical and mental health limitations, and clinical features of lupus, that warrant increased attention in future research.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".