Predicting Therapeutic Outcome of Mandibular Advancement Device Treatment in Obstructive Sleep Apnoea (PROMAD): Study Design and Baseline Characteristics
Bibliographic record
Abstract
Study Objectives: Oral appliance (OA) therapy can be an effective treatment for obstructive sleep apnea (OSA); however, there is significant uncertainty in predicting the outcome of OA therapy for an individual.Two previous studies have investigated the association between effective continuous positive airway pressure (CPAP) and OA therapy outcomes in controlled clinical research settings.The aim of this study was to examine the relationship between effective CPAP pressure and OA therapy outcome in a clinical setting.Methods: This retrospective study investigated the association between the response to OA therapy and effective CPAP pressure utilizing the same 3 criteria for response as previous studies.Effective CPAP pressure was taken from either a trial or ongoing use of CPAP.Subjects were fitted with a custom, adjustable mandibular advancement device (OA) and were sleep tested at home after acclimatization to wearing the OA and mandibular position was adjusted to maximize symptomatic response.Results: One hundred twenty subjects were included.Subjects were predominately male (85%), middle-aged (53.0 ± 9.9 y), overweight (BMI 30.3 ± 5.0 kg/m 2 ) individuals with moderate OSA (RDI 25.6 ± 18.7 events/h).Complete response to OA therapy in the 120 subjects ranged from 34% to 65% depending on response criteria.CPAP pressure was less in those responding to OA therapy (RDI < 5 events/h) 89.0 ± 1.8 cm H 2 O versus non-responders 10.1 ± 2.5 cm H 2 O, p < 0.01 with area under the ROC curve of 0.64 (95% CI 0.54-0.74),p < 0.02.A CPAP pressure ≤ 9 cm H 2 O was optimal for predicting response.Conclusions: Effective CPAP pressure is weakly associated with OA treatment outcome.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".