Sleep Disturbance in Psoriatic Disease: Prevalence and Associated Factors
Bibliographic record
Abstract
OBJECTIVE: We aimed to determine the prevalence and quality of sleep in patients with psoriatic arthritis (PsA) and those with psoriasis without PsA (PsC) followed in the same center, to identify factors associated with sleep disturbance, and to compare findings to those of healthy controls (HC). METHODS: The study included 113 PsA [ClASsification for Psoriatic ARthritis (CASPAR) criteria] and 62 PsC (PsA excluded by a rheumatologist) patients and 52 HC. Clinical variables were collected using a standard protocol. The sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). Other patient-reported outcomes collected included the Health Assessment Questionnaire (HAQ), Dermatology Life Quality Index, EQ-5D, Medical Outcomes Study Short Form-36 survey, patient's global assessment, and the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-fatigue) scale. Statistical analyses included descriptive statistics, Wilcoxon rank-sum test, and linear regression. RESULTS: The prevalence of poor sleep quality was 84%, 69%, and 50% in PsA, PsC, and HC, respectively. Total PSQI score was higher in both patients with PsA and patients with PsC compared with HC (p < 0.01) and higher in patients with PsA compared to patients with PsC (p < 0.0001). EQ-5D anxiety component, EQ-5D final, and FACIT-fatigue were independently associated with worse PSQI in patients with PsC and those with PsA (p < 0.05). Actively inflamed (tender or swollen) joints are independently associated with worse PSQI in patients with PsA (p < 0.01). CONCLUSION: Patients with psoriatic disease have poor sleep quality. Poor sleep is associated with fatigue, anxiety, and lower EQ-5D. In patients with PsA, poor sleep is associated with active joint 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".