Autonomic Dysfunction in Sleep Disorders: From Neurobiological Basis to Potential Therapeutic Approaches
Bibliographic record
Abstract
Sleep disorder has been portrayed as merely a common dissatisfaction with sleep quality and quantity. However, sleep disorder is actually a medical condition characterized by inconsistent sleep patterns that interfere with emotional dynamics, cognitive functioning, and even physical performance. This is consistent with sleep abnormalities being more common in patients with autonomic dysfunction than in the general population. The autonomic nervous system coordinates various visceral functions ranging from respiration to neuroendocrine secretion in order to maintain homeostasis of the body. Because the cell population and efferent signals involved in autonomic regulation are spatially adjacent to those that regulate the sleep-wake system, sleep architecture and autonomic coordination exert effects on each other, suggesting the presence of a bidirectional relationship in addition to shared pathology. The primary goal of this review is to highlight the bidirectional and shared relationship between sleep and autonomic regulation. It also introduces the effects of autonomic dysfunction on insomnia, breathing disorders, central disorders of hypersomnolence, parasomnias, and movement disorders. This information will assist clinicians in determining how neuromodulation can have the greatest therapeutic effects in patients with sleep disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.002 |
| 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".