Predicting Disease Activity in Rheumatoid Arthritis With the Fibromyalgia Survey Questionnaire: Does the Severity of Fibromyalgia Symptoms Matter?
Bibliographic record
Abstract
Objective To determine if the degree of baseline fibromyalgia (FM) symptoms in patients with rheumatoid arthritis (RA), as indicated by the Fibromyalgia Survey Questionnaire (FSQ) score, predicts RA disease activity after initiation or change of a disease-modifying antirheumatic drug (DMARD). Methods One hundred ninety-two participants with active RA were followed for 12 weeks after initiation or change of DMARD therapy. Participants completed the FSQ at the initial visit. The Disease Activity Score in 28 joints using C-reactive protein (DAS28-CRP) was measured at baseline and follow-up to assess RA disease activity. We evaluated the association between baseline FSQ score and follow-up DAS28-CRP. As a secondary analysis, we examined the relationship between the 2 components of the FSQ, the Widespread Pain Index (WPI) and Symptom Severity Scale (SSS), with follow-up DAS28-CRP. Multiple linear regression analyses were performed, adjusting for clinical and demographic variables. Results In multiple linear regression models, FSQ score was independently associated with elevated DAS28-CRP scores 12 weeks after DMARD initiation (B = 0.04,P= 0.01). In secondary analyses, the WPI was significantly associated with increased follow-up DAS28-CRP scores (B = 0.08,P= 0.001), whereas the SSS was not (B = −0.03,P= 0.43). Conclusion Higher levels of FM symptoms weakly predicted worse disease activity after treatment. The primary factor that informed the FSQ’s prediction of disease activity was the spatial extent of pain, as measured by the WPI.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".