Dimensions of fatigue in systemic lupus erythematosus: relationship to disease status and behavioral and psychosocial factors.
Bibliographic record
Abstract
OBJECTIVE: To characterize the experience of fatigue in patients with systemic lupus erythematosus (SLE) using a multidimensional assessment and to delineate contributors to physical and mental dimensions of fatigue. METHODS: Fatigue in 130 women with SLE was assessed using the Multidimensional Fatigue Inventory (MFI-20). Participants completed standardized questionnaires assessing sleep quality, depressed mood, social support, and leisure-time physical activity. A clinical examination determined disease activity, cumulative damage, and whether patients fulfilled American College of Rheumatology criteria for fibromyalgia (FM). A series of hierarchical multiple regressions were computed to identify contributors to physical and mental fatigue. RESULTS: Patients scored high on all 5 MFI-20 fatigue dimensions, with general fatigue and physical fatigue having the highest scores. A hierarchical multiple regression showed that greater disease damage and disease activity, the presence of FM, depressed mood, sleep disturbance, and less participation in leisure-time physical activity contributed to higher physical fatigue scores. The results of the second model found depressed mood to be the strongest determinant of mental fatigue. Disease-related variables were not associated with mental fatigue. CONCLUSION: Fatigue in SLE is multidimensional and multidetermined, with physical and mental aspects likely having different etiologies. A multidimensional assessment of fatigue in SLE is needed to tailor and optimize interventions aimed at alleviating fatigue.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".