Sleep Disorders in Connective Tissue Diseases—Coexisting Diseases or Disease Components?
Bibliographic record
Abstract
This comprehensive review examines the complex relationship between sleep disorders and rheumatic diseases, supported by findings from the latest research articles. It encompasses various rheumatic conditions, including rheumatoid arthritis, systemic lupus erythematosus, and systemic sclerosis. The review reveals the bidirectional relationship between sleep disorders and these diseases, emphasizing their impact on disease progression and quality of life. Conventional and alternative therapeutic interventions for connective tissue diseases are presented, focusing on improving sleep quality and alleviating rheumatic symptoms. The role of pro-inflammatory cytokines and their potential modulation through pharmacological agents is also discussed. In the treatment of sleep disorders, various options are proposed, such as cognitive behavioral therapy for insomnia, physical activity, dietary modifications, and alternative approaches like reflexology and acupuncture. Thus, this review offers a nuanced understanding of the connection between sleep disorders and rheumatic diseases, supported by evidence from diverse studies. Such an approach is particularly important because it enhances sleep quality for overall patient well-being in the holistic management of rheumatic conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".