Prospective Analysis of Sleep Stages, Obstructive Sleep Apnea Status, and Head and Neck Anatomy
Bibliographic record
Abstract
Objectives: 1) Determine if time in each sleep stage correlates to presence and severity of obstructive sleep apnea (OSA). 2) Analyze anatomy from nasopharyngoscopy and an otolaryngology exam for correlation with sleep stages. 3) Compare Epworth sleepiness scale (ESS), Nasal Obstruction and Septoplasty Effectiveness (NOSE) survey, and the Calgary Sleep Apnea Quality of Life Index (SAQLI) with sleep stages and OSA. Methods: Twenty‐one subjects referred for polysomnography (PSG) by a primary care provider to the Wilford Hall Ambulatory Surgery Sleep Center prospectively underwent PSG, completed three surveys (ESS, SAQLI, and NOSE), and had a head and neck examination to include Fujita and Mallampati classification, tonsil size, palatal phenotype, and percent glottis visible at rest and on jaw jet on nasopharyngoscopy. In addition to demographics and standard PSG data (AHI, RDI, RERA, and lowest oxygen saturation), time in each sleep stage was calculated. Regression modeling was used for analysis. Results: Nine of twenty‐one subjects had OSA (7 mild and 2 severe). Initial regression analysis revealed trends towards increased Stage I and II sleep and decreased stage III and rapid eye movement (REM) sleep with OSA. SAQLI scores correlated with Stage I and II sleep and inversely with Stage III and REM sleep. No anatomic data trended towards significance between OSA or primary snoring subjects. Conclusion: This pilot study suggests subjects with OSA are less likely to achieve deep sleep and have worse quality of life scores. Typical anatomy used in sleep surgery did not correlate with time in sleep stages.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".