Relationship Between Fatigue and Inflammation, Disease Duration, and Chronic Pain in Psoriatic Arthritis: An Observational DANBIO Registry Study
Bibliographic record
Abstract
OBJECTIVE: Fatigue is one of the most significant symptoms, and an outcome of great importance, in patients with psoriatic arthritis (PsA), but associations between underlying components of fatigue experienced by patients in relation to the disease have been sparsely investigated. The objectives were to describe the degree of fatigue in patients with PsA, and to examine important components associated with fatigue. METHODS: We performed a cross-sectional survey including patients registered in the Danish nationwide registry DANBIO from December 2013 to June 2014. Principal component analysis (PCA) was used to identify factors associated with fatigue. RESULTS: A total of 1062 patients with PsA were included in the study. A PCA reduced co-variables into 3 components explaining 63% of fatigue in patients. The first component, contributing to 31% of fatigue, was composed of inflammatory factors including swollen and tender joints, physician's global assessment, elevated C-reactive protein (CRP), and high Pain Detect Questionnaire (PDQ) score. The second component, contributing to 17% of fatigue, consisted of increasing age and long disease duration. The third component, contributing to 15% of fatigue, consisted of high PDQ score, tender joint count, increasing age, and concomitant low CRP, suggestive of a chronic pain component consisting of central pain sensitization or structural joint damage. CONCLUSION: Fatigue in patients with PsA may be driven by clinical inflammatory factors, disease duration, and chronic pain in the absence of inflammation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".