Efficacy of EPAP enhanced Oral Appliance Therapy in patients with Severe obstructive sleep apnea (OSA) EPAP enhanced Oral Appliance Therapy
Bibliographic record
Abstract
Introduction: Oral Appliances (OA) are a primary therapy for mild to moderate OSA patients and for CPAP intolerant severe OSA patients. A novel oral appliance, O2Vent Optima incorporates both the mandibular advancement and an air channel that circumvents nasopharyngeal obstruction. The ExVent provides oral Expiratory Positive Airway Pressure (EPAP). Previous studies have established efficacy of the combination therapy in patients with mild and moderate OSA. Our study assessed the efficacy of O2Vent Optima + ExVent for severe OSA patients who were either intolerant or declined CPAP. Materials and Methods: A multi-centre prospective, open-label study included 24 severe OSA patients (AHI>30/hr.) who declined CPAP. Average age: 54.6±5.8 years; mean BMI: 32.6±4.3; 70% were men. OA adjustment was clinically guided and optimized, highest resistance EPAP valves (7 cmH2O) were utilized. The participants used the combination therapy >80% of the nights for >12-week period. Efficacy Measures: Change in AHI between baseline vs. treatment in lab PSG. Treatment success (% of patients with ≥50% decrease in AHI from baseline). Improvement in SpO2 nadir. Results: Treatment with O2Vent Optima + ExVent reduced AHI from 37.4±10.13/hr. to 11.3±3.56/hr. (p< 0.005), average 71% reduction in AHI. The SpO2 nadir increased from 82.9±6.2% to 89.6±2.1% (p< 0.005). Treatment success rate was 84%. Conclusions: There is limited data for oral appliance therapy in patients with severe obstructive sleep apnea who are either CPAP intolerant or decline therapy. Our study demonstrated successful treatment of patients with severe obstructive sleep apnea with O2Vent Optima + ExVent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".