Incidence of anxiety, depression and insomnia in the obstructive sleep apnea population (OSA)
Bibliographic record
Abstract
Background: OSA is a common sleep disorder characterized by repeated episodes of partial or complete upper airway collapse leading to oxygen desaturation, sympathetic activation, and recurrent arousals. Patients with OSA have a prevalence of depression (35%) and anxiety (32%) and commonly complain of comorbid insomnia.2 The principal objective is to assess the prevalence of anxiety, depression, and insomnia in the adult OSA population. The secondary objective is to assess the association between these conditions and OSA severity. Methods: Retrospective chart review of 350 adults seeking oral appliance therapy (OAT) through WellSpan Health Pulmonary and Sleep Medicine (USA) between 2020 and 2022. Collected variables include age, biological sex at birth, body mass index, excessive daytime sleepiness (Epworth sleepiness scale), apnea-hypopnea index (AHI), insomnia severity score (ISS) and diagnosis of OSA, anxiety, depression, or insomnia. Results: 335 OSA patients and 15 snorers were included. Anxiety, insomnia and depression were not significantly different between OSA severity groups and snorers. There were gender and age differences between groups. The predictive model for AHI variance with linear regression included anxiety, depression, insomnia, age, gender, Epworth sleepiness scale, ISS and type of sleep test. It showed a low predictive value (small adjusted r-square of 5%). Conclusion: Anxiety, insomnia and depression were seen in our OSA sample at a prevalence of 55%, 45% and 49%, respectively. Their presence was not predictive of AHI variance but should be addressed in the disease management to improve health outcomes and quality of life.
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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) | 0.000 | 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) | 0.001 | 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".