Work Productivity Loss and Fatigue in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To explore the relationship between fatigue and work productivity loss (WPL) in people with psoriatic arthritis (PsA). METHODS: Data were collected from participants in the Utah Psoriasis Initiative Arthritis registry between January 2010 and May 2013. WPL was measured with the 8-item Work Limitations Questionnaire. Fatigue was assessed with question 1 from the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI#1), "How would you describe the overall level of fatigue/tiredness you have experienced?" and with question 1 from the Psoriatic Arthritis Quality of Life Questionnaire (PsAQOL#1) "I feel tired whatever I do." Psoriatic activity was evaluated with tender joint count (TJC), swollen joint count (SJC), dactylitis count, enthesitis count, inflammatory back pain (IBP), physician global assessment, body surface area, and psoriasis pain and itch. RESULTS: Among 107 participants, work productivity was reduced by 6.7%, compared to benchmark employees without limitations. Fatigue was reported by 54 patients (50.5%) on PsAQOL#1, and 64 (60.0%) were classified as high fatigue by BASDAI#1. TJC, SJC, enthesitis count, IBP, and depressed mood were highest or most frequent in participants reporting fatigue. After adjustments for psoriatic activity and depressed mood, WPL was associated with fatigue, as measured by PsAQOL#1 (p = 0.01) and BASDAI#1 (p = 0.002). CONCLUSION: WPL was associated with fatigue, and the association was not entirely explained by the evaluated musculoskeletal, cutaneous, or psychiatric manifestations of PsA.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| 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".