Mandibular width as a novel anthropometric measure for assessing obstructive sleep apnea risk
Bibliographic record
Abstract
Craniofacial abnormalities are a known obstructive sleep apnea (OSA) risk factor, but still need to be better characterized. This study investigates the relationship between mandibular width and the risk of developing OSA.We retrospectively analyzed 3D reconstructions of head and neck computed tomography (CT) scans at our institution for mandibular width, neck circumference, neck fat volume (NFV), airway volume (AWV), and NFV:AWV ratio. Age, gender, and BMI were also documented. Patients were contacted to complete a STOP-BANG survey to assess OSA risk. Only patients with reconstructable scans and completed STOP-BANG questionnaires were included in the study. Survey results were analyzed to assess the correlation between mandible width and STOP-BANG. Mandible association was also compared to the associations of the other known risk factors.The final analysis included 427 patients with a mean age of 58.98 years (standard deviation = 16.77), 56% of whom were male. Mandibular width was found to positively correlate with STOP-BANG score (r = .416, P < .001). Statistically significant differences between mandible size for each risk group was seen (P < .001). After controlling for age and sex, mandible size was significantly different only for the low risk vs. high risk groups (odds ratio = 1.11; 95% confidence interval = 1.03-1.20; P = .007). Furthermore, when stratified according to mandible size, the small mandible group (<77.50 mm) predominantly consisted of low risk patients; the medium size mandible group (77.50-84.40 mm) was predominated by intermediate risk patients, and large mandible (>84.40 mm) was predominantly seen in high risk patients. Mandible width expressed a stronger association than NFV:AWV ratio, but neck circumference and NFV had stronger associations than did mandible width.In addition to previously documented OSA risk factors, mandibular width is positively correlated with OSA as an independent risk factor. Observation of a wide mandible (jaw) should raise awareness of OSA risk and increase screening methods when appropriate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".