Clinical correlates of sleep problems in systemic sclerosis: the prominent role of pain
Bibliographic record
Abstract
OBJECTIVE: Problems with sleep are common in patients with SSc and impact daily function. Little research, however, has examined factors associated with sleep disruption in SSc. Therefore, the objective of this study was to investigate socio-demographic and medical factors associated with sleep disruption in SSc. METHODS: Cross-sectional study of 70 patients from one Canadian Scleroderma Research Group site who were assessed with a 100-mm sleep disruption visual analogue scale (VAS). Patients also completed measures of pain and depressive symptoms and underwent clinical histories and medical examinations. Pearson's correlations were used to assess bivariate association of socio-demographic and medical variables with sleep VAS scores. Multivariable associations of socio-demographic (Step 1) and medical (Step 2) variables with sleep VAS scores were assessed using hierarchical multiple linear regression. RESULTS: The mean (s.d.) sleep disruption VAS score was 38.5 (29.9). In bivariate analyses, sleep disruption was associated with marital status (r = -0.24, P = 0.042), smoking (r = 0.27, P = 0.025), gastrointestinal symptoms (r = 0.27, P = 0.023), breathing problems (r = 0.31, P = 0.009), pain (r = 0.53, P < 0.001) and symptoms of depression (r = 0.34, P = 0.004). In multivariate analysis, only marital status (standardized β = -0.24, P = 0.049) and pain (standardized β = 0.50, P < 0.001) were significantly associated with sleep disruption. CONCLUSION: Sleep disruption scores were as high in SSc as in RA and higher than in the general population. Pain was robustly associated with sleep disruption. Additional research is needed on sleep in SSc so that well-informed sleep interventions can be developed and tested.
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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 | 0.001 | 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) | 0.002 | 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".