Rheumatoid Arthritis and Fibromyalgia: A Frequent Unrelated Association Complicating Disease Management
Bibliographic record
Abstract
OBJECTIVE: To assess the value of the 28-joint Disease Activity Score (DAS28) in evaluating disease activity in rheumatoid arthritis (RA) associated with fibromyalgia (FM). In this situation, because of the weight of the subjective measures included in the DAS28 equation, the patient's status may be overestimated, leading to inappropriate treatment. We analyze the relationship between RA and FM and discuss whether the association is random or a marker of poor prognosis. METHODS: A questionnaire, developed when biologic therapies were introduced, was administered and the results analyzed in a consecutive, female outpatient population including 105 patients with RA, 49 with RA and FM (RAF), and 28 with FM. Psychosocial characteristics, disease presentation, and radiographic joint destruction evaluation were compared in the 3 populations. RESULTS: The presentation of RA was the same in patients with RA and RAF, but the 2 populations differed by socioprofessional characteristics, significantly higher disease activity in patients with RAF, and significantly more severe joint destruction in patients with RA. The RAF group was similar to the FM control population in socioprofessional and some physical characteristics. Regression analysis using the DAS28 measures differed significantly in the weight allowed to 28-joint counts for pain and swelling, but the constant factor was higher in patients with RAF. CONCLUSION: DAS28 overestimated objective RA severity in patients who also had FM. The association between RA and FM does not appear to be a marker of worse prognosis, but rather a fortuitous association between the 2 diseases and one that may afford these patients some protection against joint destruction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".