Arousal Intensity is a Distinct Pathophysiological Trait in Obstructive Sleep Apnea
Bibliographic record
Abstract
STUDY OBJECTIVES: Arousals from sleep vary in duration and intensity. Accordingly, the physiological consequences of different types of arousals may also vary. Factors that influence arousal intensity are only partly understood. This study aimed to determine if arousal intensity is mediated by the strength of the preceding respiratory stimulus, and investigate other factors mediating arousal intensity and its role on post-arousal ventilatory and pharyngeal muscle responses. METHODS: Data were acquired in 71 adults (17 controls, 54 obstructive sleep apnea patients) instrumented with polysomnography equipment plus genioglossus and tensor palatini electromyography (EMG), a nasal mask and pneumotachograph, and an epiglottic pressure sensor. Transient reductions in CPAP were delivered during sleep to induce respiratory-related arousals. Arousal intensity was measured using a validated 10-point scale. RESULTS: > 0.10, P ≤ 0.006). High (> 5) intensity arousals caused greater ventilatory responses than low (≤ 5) intensity arousals (10.9 [6.8-14.5] vs. 7.8 [4.7-12.9] L/min; P = 0.036) and greater increases in tensor palatini EMG (10 [3-17] vs. 6 [2-11]%max; P = 0.031), with less pronounced increases in genioglossus EMG. CONCLUSIONS: Average arousal intensity is independent of the preceding respiratory stimulus. This is consistent with arousal intensity being a distinct trait. Respiratory and pharyngeal muscle responses increase with arousal intensity. Thus, patients with higher arousal intensities may be more prone to respiratory control instability. These findings are important for sleep apnea pathogenesis.
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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".