Effect of Fatigue on Health-Related Quality of Life and Work Productivity in Psoriatic Arthritis: Findings From a Real-World Survey
Bibliographic record
Abstract
Objective To evaluate fatigue frequency and severity among patients with psoriatic arthritis (PsA) and assess the effect of fatigue severity on patient-reported outcome measures (PROMs) assessing quality of life, function, and work productivity. Methods Data were derived from the Adelphi Disease Specific Programme, a cross-sectional survey conducted in 2018 in the United States and Europe. Patients had physician-confirmed PsA. Fatigue was collected as a binary variable and through its severity (0-10 scale, using the 12-item Psoriatic Arthritis Impact of Disease fatigue question) from patients; physicians also reported patient fatigue (yes/no). Other PROMs included the 5-level EuroQol 5-dimension questionnaire (EQ-5D-5L) for health-related quality of life (HRQOL), Health Assessment Questionnaire–Disability Index, and Work Productivity and Activity Impairment Questionnaire. Multivariate linear regression was used to evaluate the association between fatigue severity and other PROMs. Results Among the 831 included patients (mean age 47.5 yrs, mean disease duration 5.3 yrs, 46.9% female, 48.1% receiving a biologic), fatigue was reported by 78.3% of patients. Patients with greater fatigue severity had greater disease duration, PsA severity, pain levels, body surface area affected by psoriasis, and swollen and tender joint counts (all P < 0.05). In multivariate analyses, patients with greater fatigue severity experienced worse physical functioning, HRQOL, and work productivity (all P < 0.001). Presence of fatigue was underreported by physicians (reported in only 32% of patients who self-reported fatigue). Conclusion Prevalence of patient-reported fatigue was high among patients with PsA and underrecognized by physicians. Fatigue severity was associated with altered physical functioning, work productivity, and HRQOL.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".