Obstructive Sleep Apnea Knowledge Among Dentists and Physicians
Bibliographic record
Abstract
Study Objectives: Obstructive sleep apnea (OSA) is a largely undiagnosed and untreated sleep disorder with public health implications. This study investigated whether dentists were as knowledgeable about OSA as physicians. Methods: Two convenience samples of California dentists were surveyed online (N=107) and in-person (N=63) between January and April 2019 using the 18 knowledge items from the validated Obstructive Sleep Apnea Knowledge and Attitudes (OSAKA) Questionnaire. California dentists' total score was then compared to a compilation of published physicians' total OSA-knowledge scores from 12 studies (2003-2020) using Chi-square tests with Bonferroni adjusted p < 0.0023. OSA-knowledge gaps and competencies were also compared on individual item data provided for nine of the published physician studies. Results: Mean total correct OSA-knowledge scores were 73.6% for California dentists (N=170) and 63.9% across all physicians (N=2,559); scores were 84.5% for Canadian otolaryngology residents (N=66), 75.6% for U.S. physicians (N=305), and 62.3% for all other non-U.S. physicians (N=2,188). The all-physician group had more knowledge gaps than dentists. Conclusion: Dentists had noninferior knowledge of OSA compared with most physician groups. Findings suggest that dentists may serve to increase the number of healthcare providers able to identify and treat patients with OSA, mitigating this healthcare gap. Suboptimal sleep medicine and OSA training in medical and dental education remains a challenge, perpetuating the public health ramifications of underdiagnosed and undertreated OSA. Clinical Implications: Engaging more dentists to identify patients at risk for OSA at the point of care and treat or refer patients for treatment, as appropriate, helps meet this public health need.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".