Clinical association between current depressive symptoms and odds ratio product in US sleep centers
Bibliographic record
Abstract
Introduction: The Odds Ratio Product (ORP) is a validated EEG-based measure of sleep depth, more sensitive than traditional metrics. While it has been studied in healthy individuals and those with sleep-wake disorders, its relevance in psychiatric conditions remains unclear. This study examined ORP during sleep and its association with depressive symptoms in a large cohort referred to multiple U.S. sleep centers. Methods: We retrospectively analyzed data from 829 adults (48.85% female; mean age 43.49 ± 13.74 years) enrolled in two multicenter studies. Each participant completed the Patient Health Questionnaire-9 (PHQ-9) and underwent overnight polysomnography (PSG), with ORP calculated from central EEG channels. Mean and standard deviation ORP values were derived for the full night and Wake, stages 1, 2, 3, and REM sleep. Associations between ORP metrics and depression severity (PHQ-9 total and PHQ-9 ≥10) were tested using linear and logistic regressions, adjusting for age and sex. Model fit was assessed with the Akaike Information Criterion (significance level α = 0.05). Results: Fixed-effects models outperformed mixed-effects models. Mean ORP during the full night and light sleep (stages 1 + 2) showed a significant U-shaped association with depression, indicating both high and low ORP values relate to greater depressive burden. In stage 3, higher mean ORP was linearly associated with more severe symptoms. Lower ORP variability across the night also correlated with higher depression scores. Conclusions: ORP shows potential as a non-invasive biomarker for depressive symptoms, with distinct associations depending on sleep depth. Integrating ORP into clinical PSG analyses could improve detection of depression-related sleep patterns.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".