Upper-Airway Collapsibility and Loop Gain Predict the Response to Oral Appliance Therapy in Patients with Obstructive Sleep Apnea
Bibliographic record
Abstract
Abstract Rationale Oral appliances (OAs) are commonly used as an alternative treatment to continuous positive airway pressure for patients with obstructive sleep apnea (OSA). However, OAs have variable success at reducing the apnea–hypopnea index (AHI), and predicting responders is challenging. Understanding this variability may lie with the recognition that OSA is a multifactorial disorder and that OAs may affect more than just upper-airway anatomy/collapsibility. Objectives The objectives of this study were to determine how OA alters AHI and four phenotypic traits (upper-airway anatomy/collapsibility and muscle function, loop gain, and arousal threshold), and baseline predictors of which patients gain the greatest benefit from therapy. Methods In a randomized crossover study, 14 patients with OSA attended two sleep studies with and without their OA. Under each condition, AHI and the phenotypic traits were assessed. Multiple linear regression was used to determine independent predictors of the reduction in AHI. Measurements and Main Results OA therapy reduced the AHI (30 ± 5 vs. 11 ± 2 events/h; P < 0.05), which was driven by improvements in upper-airway anatomy/collapsibility under passive (1.9 ± 0.7 vs. 4.7 ± 0.6 L/min; P < 0.005) and active conditions (2.4 ± 0.9 vs. 6.2 ± 0.4 L/min; P < 0.001). No changes were seen in muscle function, loop gain, or the arousal threshold. Using multivariate analysis, baseline passive upper-airway collapsibility and loop gain were independent predictors of the reduction in AHI (r2 = 0.70; P = 0.001). Conclusions Our findings suggest that OA therapy improves the upper-airway collapsibility under passive and active conditions. Importantly, a greater response to therapy occurred in those patients with a mild anatomic compromise and a lower loop gain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".