A Novel Use of Complete Denture Prosthesis as Mandibular Advancement Device in the Treatment of Obstructive Sleep Apnea in Edentulous Subjects
Bibliographic record
Abstract
W e live in an exciting time to be a dentist who provides oral appliance therapy (OAT) for obstructive sleep apnea (OSA), and the American Academy of Dental Sleep Medicine (AADSM) is in the midst of a strategic stage in its growth and development as the leading professional society for dentists who treat sleep-disordered breathing.More people than ever before are in need of our expertise and care.Data from the Wisconsin Sleep Cohort suggest that the prevalence of OSA has increased substantially over the last two decades, likely due to the widespread obesity epidemic in the U.S. 1 The most current prevalence estimates are that 26 percent of adults between 30 and 70 years of age have at least mild OSA, and 10 percent of adults have moderate or severe disease.Based on this data, it has been estimated that OSA now afflicts at least 25 million adults in the U.S. 2 Addressing this growing burden of OSA, we now have more dentists than ever before who are practicing dental sleep medicine.In the span of about two-and-a-half decades, the AADSM has grown from a small group to an expansive fellowship of colleagues, with a membership of more than 3,000 individuals.More dentists in more places throughout the U.S. and abroad are offering OAT, a treatment that is life-changing for many and that has the potential to be life-saving. 3 Although we celebrate the rapid and necessary growth of dental sleep medicine, and recognize the contributions of those leaders who paved the way for us, we also must recognize the challenges that are both before us and on the horizon.Currently, the AADSM Board of Directors is in the initial stages of a strategic planning process that will address the most pressing concerns facing our organization and our membership.As we embark on this journey in the first half of my tenure as 2014-2016 president of the AADSM, I anticipate that we will spend much of our time considering and discussing competition, connection and collaboration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".