Utility of Acoustic Pharyngometry for the Diagnosis of Obstructive Sleep Apnea
Bibliographic record
Abstract
Abstract Rationale Owing to resource limitations, the testing of patients for obstructive sleep apnea (OSA) is often delayed. There is a need to accurately triage and expedite testing in those with a high pretest probability of OSA. Acoustic pharyngometry is a simple, noninvasive technique used to assess the upper airway cross-sectional area (UA-XSA), which is known to be reduced in those with OSA. Objectives To determine the discriminative ability and predictive value of UA-XSA measurements by acoustic pharyngometry for OSA. Methods We conducted a cross-sectional study with a clinical cohort of consecutive adults with suspected OSA who had undergone both polysomnography and acoustic pharyngometry. OSA was defined as an apnea–hypopnea index greater than or equal to 5. Multivariable logistic regression analyses and receiver operating characteristic curves were used. Measurements and Main Results: The cohort included 576 subjects, 87% of whom had OSA and 64% of whom were men. The subjects’ median body mass index (BMI) was 30.3 kg/m2, and their median age was 57 years. The median UA-XSA at FRC when sitting was significantly smaller in those with OSA compared with those without OSA (3.3 cm2 [interquartile range, 2.7–3.8] vs. 3.7 cm2 [interquartile range, of 2.9–4.2]). When the analysis was controlled for age, sex, BMI, and comorbidities, the odds of OSA increased for every 1-cm2 decrease in the mean UA-XSA FRC when sitting (odds ratio, 1.62; 95% confidence interval, 1.23–2.13). The mean UA-XSA provided fair discrimination for OSA (area under the curve, 0.60). A cutoff value of 3.75 cm2, the point with the best sum of sensitivity and specificity, had sensitivity of 73% and specificity of 46%. The magnitude of the incremental discriminative value of UA-XSA over clinical variables (age, sex, BMI, and comorbidities) was small and nonsignificant (P = 0.5). Conclusions The mean UA-XSA at FRC when sitting or supine provided no further significant advantage over clinical variables for the discernment of OSA.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".