Bibliographic record
Abstract
PURPOSE OF REVIEW: We have long assumed that rheumatic pain causes sleep problems, fatigue, and functional disability. This paper reviews the accumulating evidence from human and animal experimental research studies that show a bidirectional relationship of disordered sleep to pain and fatigue. RECENT FINDINGS: The studies demonstrate that both disturbances of sleep and sleep restriction result in increased sensitivity to noxious stimuli and musculoskeletal pain symptoms. The notion of central nervous system hypersensitivity affecting widespread pain in patients with fibromyalgia syndrome is the result of a reduction in neurophysiologic inhibition of perception of noxious stimuli that is provoked by disordered sleep. Clinical and epidemiological studies show that sleep disturbances directly influence musculoskeletal pain, fatigue, mood, and overall well-being. Indeed, the interrelationships of the sleeping/waking brain with cytokine and cellular immune functions have important implications for the understanding of rheumatic disease pathology and management with disease-modifying antirheumatic drugs. SUMMARY: The determination of how disordered sleep affects musculoskeletal pain, fatigue, mood, and behavior is important in the assessment and management of patients with rheumatic illness. The high prevalence of obstructive sleep apnea and restless legs syndromes requires more research to determine whether treatments of these sleep disorders will benefit the symptoms of rheumatic diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".