General Dentists and Dental Specialists’ Knowledge of Treatment, Diagnosis, Referral, and Risk Factors of Obstructive Sleep Apnea: A Systematic Review
Bibliographic record
Abstract
Objectives: This systematic review aimed to evaluate general dentists and dental specialists’ knowledge regarding obstructive sleep apnea (OSA) diagnosis, referral, risk factors, and treatment. Methods: A systematic search of databases, including Web of Science, PubMed, and ProQuest, was conducted for studies published up to 25 September 2023, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Inclusion criteria included cross-sectional studies that assessed the knowledge of general dentists or dental specialists. A quality assessment was performed using the Newcastle–Ottawa Quality Assessment Scale. Results: The seven included studies demonstrated varied knowledge levels among respondents regarding polysomnography as the gold standard for diagnosing OSA, with percentages ranging from 40.18% to 90%. While recognition of craniofacial structure as a risk factor for OSA was consistently high, knowledge about body weight as a risk factor varied. Additionally, the understanding of continuous positive airway pressure as the standard treatment showed discrepancies across the studies. Conclusions: Given that some of the included articles displayed a moderate to high risk of bias, the results highlight the varying levels of OSA knowledge among dentists and specialists across the studies. This indicates a potential need for targeted educational programs to improve their understanding and management of OSA.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".