Is Fatigue an Inflammatory Variable in Rheumatoid Arthritis (RA)? Analyses of Fatigue in RA, Osteoarthritis, and Fibromyalgia
Bibliographic record
Abstract
OBJECTIVE: To investigate whether fatigue is an inflammatory (rheumatoid arthritis; RA) variable, the contributions of RA variables to fatigue, and the levels of fatigue in RA compared with osteoarthritis (OA) and fibromyalgia (FM). METHODS: We studied 2096 RA patients, 1440 with OA, and 1073 with FM in a clinical setting, and 14,607 RA, 3173 OA, and 2487 patients with FM in survey research. We partitioned variables into inflammatory and noninflammatory factors and examined variable contribution to fatigue (0-10 visual analog scale). RESULTS: Factor analysis identified Disease Activity Score-28 (DAS28) and swollen (SJC) and tender joint count (TJC) as a physician-inflammation factor, and patient global assessment, pain, Health Assessment Questionnaire, and fatigue as patient components. Fatigue demonstrated weak correlations with erythrocyte sedimentation rate (ESR; r = 0.071) and SJC (r = 0.112), weak to fair correlations with TJC (r = 0.294), physician global assessment of RA activity (r = 0.384), and DAS28 (r = 0.399), but strong correlation with patient global assessment of severity (r = 0.567). In hierarchical regression analysis, patient global explained 43.1% of DAS28 fatigue variance; when SJC, TJC, and ESR were entered, the explained variance increased to 43.7%. In reverse order, SJC, TJC, and ESR explained 9.2% of the variance, but explained variance increased to 43.7% when patient global was added. The mean clinic fatigue scores were RA 4.9, OA 4.8, FM 7.6; mean survey scores were RA 4.5, OA 4.4, FM 6.3. Adjusted for age and sex, RA and OA fatigue scores were not significantly different. CONCLUSION: Inflammatory components of the DAS28 contribute minimally to fatigue. RA and OA fatigue levels do not differ. Fatigue is not an inflammatory variable and has no unique association with RA or RA therapy.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".