Immediate postarousal sleep dynamics: an important determinant of sleep stability in obstructive sleep apnea
Bibliographic record
Abstract
Arousability from sleep is increasingly recognized as an important determinant of the clinical spectrum of sleep disordered breathing (SDB). Patients with SDB display a wide range of arousability. The reason for these differences is not known. We hypothesized that differences in the speed with which sleep deepens following arousals/awakenings (postarousal sleep dynamics) is a major determinant of these differences in arousability in patients with SDB. We analyzed 40 preexisting clinical polysomnography records from patients with a range of SDB severity (apnea-hypopnea index 5-135/h). Sleep depth was determined every 3 s using the odds ratio product (ORP) method, a continuous index of sleep depth (0 = deep sleep, 2.5 = full wakefulness) that correlates strongly (r = 0.98) with arousability (Younes M, Ostrowski M, Soiferman M, Younes H, Younes M, Raneri J, and Hanly P. Sleep 38: 641-654, 2015). Time course of ORP was determined from end of arousal until the next arousal. All arousals were analyzed (142 ± 65/polysomnogram). ORP increased from 0.58 ± 0.32 during sleep to 1.67 ± 0.35 during arousals. ORP immediately (first 9 s) following arousals/awakenings (ORP-9) ranged from 0.21(very deep sleep) to 1.71 (highly arousable state) in different patients. In patients with high ORP-9, sleep deepened slowly (over minutes) beyond 9 s but only if no arousals/awakenings recurred. ORP-9 correlated strongly with average non-rapid eye movement sleep depth (r = 0.87, P < 2E-13), the arousal/awakening index (r = 0.68, P < 5E-6), and with the apnea-hypopnea index (r = 0.60, P < 0.001). ORP-9 was consistent within each patient and did not change on continuous positive airway pressure despite marked improvement in sleep architecture. We conclude that postarousal sleep dynamics are highly variable among patients with sleep-disordered breathing and largely determine average sleep depth and continuity.
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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 | 0.001 |
| 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".