Determinants of sleep quality in women with systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: To characterize sleep complaints in women with systemic lupus erythematosus (SLE) and to identify correlates of sleep quality. METHODS: Sleep quality in 100 women with SLE was assessed using the Pittsburgh Sleep Quality Index (PSQI). Participants completed standardized questionnaires assessing depressed mood, leisure time physical activity, functional disability, and pain severity. A clinical examination determined disease activity, cumulative damage, and whether patients fulfilled the American College of Rheumatology criteria for fibromyalgia. A series of hierarchical multiple regressions were computed. RESULTS: The mean +/- SD global PSQI score was 6.98 +/- 4.03, with moderate to severe sleep impairment reported by 56% of the sample. The first model testing the importance of demographic factors was not statistically significant. In the disease-related model, the use of prednisone and functional disability both contributed to poor sleep quality (P < 0.001). The addition of level of exercise participation to the demographic set significantly added to the model (P = 0.001). Depression significantly added to the demographic set, explaining 29% of the variance (P < 0.0001). When these variables, along with disease related variables, were simultaneously regressed on the PSQI Global Score, only depressed mood appeared as a significant independent determinant of global sleep quality (P < 0.001). However, the point estimates for the Beta coefficients were consistent with effects for lack of exercise and prednisone use. CONCLUSION: A significant proportion of women with SLE suffer from poor sleep quality. The findings suggest that depressed mood, prednisone use, and lack of exercise contribute to decreased overall sleep quality.
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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 | 0.003 |
| 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".