Prevalence and Polysomnographic Correlates of Insomnia Comorbid with Medical Disorders
Bibliographic record
Abstract
STUDY OBJECTIVES: To determine the prevalence and polysomnographic correlates of insomnia in subjects with self-reported medical disorders. DESIGN: Prospective cross-sectional study. PARTICIPANTS: Community-based sample of 3282 men and women aged 18 to 65 years old, with a subset who underwent polysomnography. MEASUREMENTS: Self-reported measures of sleep habits and current health, and polysomnographic sleep variables. RESULTS: The prevalence of insomnia was 21.4%. The adjusted odds of insomnia were 2.2 times as high in persons with any medical disorders as in those without medical disorders. Specifically, odds of insomnia were higher in people with heart disease (OR = 1.6 [95% CI: 1.2-23], P = 0.004), hypertension (1.5 [12-18], P < 0.001), diabetes (1.4 [105-20], P = 0.04), stomach ulcers (2.1 [1.6-2.7], P < 0.001), arthritis (1.8 [1.5-2.2], P < 0.001), migraine (1.8 [1.5-2.1], P < 0.001), asthma (1.6 [1.3-2.0], P = 0.04), COPD (1.9 [1.5-2.5], P < 0.001), neurological problems (2.0 [1.5-2.7], P < 0.001), and menstrual problems (1.7 [1.3-2.1], P < 0.001) than in people without these disorders. Prevalence of insomnia increased with increasing number of medical disorders. However, polysomnographic sleep was not significantly different in persons with or without medical disorders for most disorders assessed. CONCLUSION: This large population-based study suggests that insomnia is highly prevalent in diverse chronic medical disorders. However, polysomnographic evidence of disturbed sleep is present in only a subset of comorbid insomnia populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".