Snoring Vibration: A Robust Measure for Predicting Treatment Outcome for Obstructive Sleep Apnea
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) represents a prevalent condition impacting over 9% of the general adult population. Various treatment options have been clinically proposed and utilized, with a particular focus on continuous positive airway pressure (CPAP) and oral appliances due to their overall effectiveness and higher adherence rates. CPAP therapy has demonstrated greater effectiveness but lower adherence compared to oral appliances. However, treatment success of oral appliances is not always guaranteed, hence sleep physicians are more cautious in their prescriptions unless they can reasonably estimate the chance of responding to oral appliance therapy. Prior studies often rely on invasive or inconvenient methodologies such as drug-induced sleep endoscopy (DISE), cephalometry, multisensor catheters, or full polysomnography (PSG). In this prospective study, we collected data with a home sleep apnea test (HSAT) device from 50 participants (38 using mandibular advancement devices [MADs] and 12 using tongue stabilizing devices [TSDs]). We used a simple yet informative data source: snoring vibrations extracted from a nasal pressure sensor with a low sampling frequency (125 Hz). Using spectro-temporal analysis of the snoring signal, we successfully predicted therapy efficacy with accuracies of 88% for MAD and 91% for TSD. Our proposed methodology presents a promising approach that can be utilized without further need for PSG or integrated within PSG testing for accurate prediction of oral appliance efficacy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".