Fibromyalgia, Systemic Lupus Erythematosus (SLE), and Evaluation of SLE Activity
Bibliographic record
Abstract
OBJECTIVE: To determine if fibromyalgia (FM) or fibromyalgia-ness (the tendency to respond to illness and psychosocial stress with fatigue, widespread pain, general increase in symptoms, and similar factors) is increased in patients with compared to those without systemic lupus erythematosus (SLE); to determine whether FM or fibromyalgia-ness biases the SLE Activity Questionnaire (SLAQ); and to determine if the SLAQ is overly sensitive to FM symptoms. METHODS: We developed a 16-item SLE Symptom Scale (SLESS) modeled on the SLAQ and used that scale to investigate the relation between SLE symptoms and fibromyalgia-ness in 23,321 patients with rheumatic disease. FM was diagnosed by survey FM criteria, and fibromyalgia-ness was measured using the Symptom Intensity (SI) Scale. As comparison groups, we combined patients with rheumatoid arthritis and noninflammatory rheumatic disorders into an "arthritis" group and also utilized a physician-diagnosed group of patients with FM. RESULTS: FM was identified in 22.1% of SLE and 17.0% of those with arthritis. The SI scale was minimally increased in SLE. The correlation between SLAQ and SLESS was 0.738. SLESS/SLAQ scale items (Raynaud's phenomenon, rash, fever, easy bruising, hair loss) were significantly more associated with SLE than FM, while the reverse was true for headache, abdominal pain, paresthesias/stroke, fatigue, cognitive problems, and muscle pain or weakness. There was no evidence of disproportionate symptom-reporting associated with fibromyalgia-ness. Self-reported SLE was associated with an increased prevalence of FM that was unconfirmed by physicians, compared to SLE confirmed by physicians. CONCLUSION: The prevalence of FM in SLE is minimally increased compared with its prevalence in patients with arthritis. Fibromyalgia-ness does not bias the SLESS and should not bias SLE assessments, including the SLAQ.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".