Use of the Complete Airway Repositioning and Expansion (CARE) approach in 220 patients with Obstructive Sleep Apnea (OSA): A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE/BACKGROUND: Obstructive sleep apnea (OSA) is a prevalent disease with significant health repercussions. While many effective OSA treatment modalities exist, Complete Airway Repositioning and Expansion (CARE) represents an emerging approach that leverages gradual airway expansion, with or without mandibular advancement. We conducted a retrospective study of patients who underwent CARE with a dental provider and examined how their sleep study data changed, with a focus on apnea hypopnea index (AHI). PATIENTS/METHODS: A retrospective database of 220 adult patients was examined. Demographic data and radiographic and sleep study data were compared in patients before and following at least 6 months of treatment with one of two possible dental devices. RESULTS: The median age of patients in this cohort was 50 years, and evenly split by gender. The median decrease in AHI was 49.0%, with a median pre-treatment AHI of 17.3 and median post-treatment AHI of 9.6 (p<0.001). Most participants (63.6%) demonstrated an improvement in their OSA severity class. Fifty-seven (25.9%) participants had complete resolution of their OSA. Post-treatment, 151 (68.6%) of patients had OSA severities of none or mild. Thirty-four (15.5%) of patients had in increase in AHI and 13 (6.0%) of these patients demonstrated an increase in OSA classification. One patient experienced an adverse event in the form of a loose molar tooth requiring repair. Overall findings were limited by missingness of BMI and clinical co-morbidity data, as well as quality of life measures. CONCLUSIONS: In this large, but data limited retrospective series, CARE seems to be an effective and safe approach to OSA management that may be a useful alternative to current mainstays of OSA management. Further investigation is warranted.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".