Treating <scp>OSA</scp>: <scp>C</scp>urrent and emerging therapies beyond <scp>CPAP</scp>
Bibliographic record
Abstract
Continuous positive airway pressure (CPAP) is the standard treatment for moderate-to-severe obstructive sleep apnoea (OSA). However, adherence to CPAP is limited and non-CPAP therapies are frequently explored. Oral appliance (OA) therapy is currently widely used for the treatment of snoring, mild, moderate and severe OSA. The most commonly used and studied OA consists of a maxillary and mandibular splint which hold the lower jaw forward during sleep. The efficacy of OA is inferior to CPAP; however, the effectiveness as measured by sleepiness, quality of life, endothelial function and blood pressure is similar likely due to higher acceptance and subjective adherence. Upper airway stimulation augments neural drive by unilaterally stimulating the hypoglossal nerve. The Stimulation Therapy for Apnea Reduction (STAR) study enrolled 126 patients and demonstrated a 68% reduction in OSA severity. A high upfront cost and variable response are the main limitations. Oropharyngeal exercises consist of a set of isometric and isotonic exercises involving the tongue, soft palate and lateral pharyngeal wall. The collective reported trials (n = 120) showed that oropharyngeal exercises can ameliorate OSA and snoring (~30-40%). Nasal EPAP devices consist of disposable one-way resister valve. A systematic review (n = 345) showed that nasal EPAP reduced OSA severity by 53%. The Winx device consists of a mouthpiece placed inside the oral cavity that is connected by tubing to a console that generates negative pressure. Winx may provide successful therapy for ~30-40% of OSA patients. In conclusion, several non-CPAP therapies to treat OSA are currently available.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".