Interhemispheric sleep depth coherence predicts driving safety in sleep apnea
Bibliographic record
Abstract
Obstructive sleep apnea is associated with increased risk of car crashes; however, conventional measures of sleep apnea severity do not clearly identify those individuals who are at greatest risk. Here we tested whether, among individuals with sleep apnea, those with reduced interhemispheric sleep depth coherence, measured by correlation between right and left hemisphere odds ratio product, are at greater risk. The sample was derived from the Sleep Heart Health Study, a prospective observational cohort study, and included 1,378 adults with sleep apnea. The occurrence of a car crash was ascertained by a questionnaire administered 2 years after the sleep study, which asked about the occurrence of crashes during the year prior to questionnaire administration. We computed the sleep depth coherence from electroencephalograms recorded during baseline sleep studies and after 5 years. The weighted kappa coefficient and Bangdiwala's B were 0.34 and 0.59, respectively, indicating a fair to moderate stability over a 5-year interval. Multivariate logistic regression, adjusted for age, sex, race, body mass index and miles driven per year, was used to assess the risk of a car crash. Compared to the lowest quartile of sleep depth coherence (<0.86), individuals in the highest quartile (>0.93) had a 62% (95% confidence interval, 22%-81%) lower risk of an accident. Further adjustments for usual sleep duration and sleepiness did not meaningfully alter these findings. Higher interhemispheric sleep depth coherence is associated with significantly lower risk of motor vehicle crashes in individuals with sleep apnea. This suggests that high interhemispheric sleep depth coherence may be a marker of resistance to sleep apnea-related adverse neurocognitive outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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 teacher head, 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".