Systemic Lupus and Risk of Restless Legs Syndrome
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of restless legs syndrome (RLS) in women with systemic lupus erythematosus (SLE), and to compare this to a rheumatic disease sample without SLE. METHODS: Unselected consecutive female patients were SLE were recruited from a lupus clinic. A RLS questionnaire based on 4 criteria, validated by the International Restless Legs Syndrome Study Group, was administered during a face-to-face interview. Smoking history and height and weight data were collected. Similar methods were used to determine RLS prevalence in a comparator group of women with rheumatic diseases other than SLE. Controls were frequency-matched by age group (in 5-year age bands) to SLE subjects. Controls were otherwise unselected. RESULTS: We recruited 33 women with SLE and 32 controls. Twelve of 33 female SLE subjects scored positively for RLS (37.5%; 95% CI 22.9, 54.7) compared to 4 of 32 controls (12.5%; 95% CI 5.0, 28.1). Multivariate logistic regression showed that adjusted for age, obesity, and smoking, women with SLE were more likely to have RLS than the female controls (adjusted odds ratio 6.61, 95% CI 1.52, 28.77). In our multivariate analyses of all rheumatic patients, including SLE, the adjusted OR for obesity and RLS was 5.14 (95% CI 1.07, 24.6). CONCLUSION: These novel data indicate that RLS is more prevalent in women with SLE than in controls. Although obesity was a significant risk factor for RLS in our sample, the predictive covariates examined were limited.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".