Comparison of screening methods for obstructive sleep apnea in the context of dental clinics: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To review the available bibliographic data to identify the best screening methods to detect potential obstructive sleep apnea (OSA) patients during dental clinical practice. METHODS: Relevant studies published up to April 2020 were sourced from PubMed, Embase, MEDLINE, Cochrane, and LILACS databases. RESULTS: Thirty studies were selected. For adults, the screening methods available to the dentist included questionnaires, scales, indexes, pulse oximetry, and anatomical factors. A combination of questionnaires is potentially the most reliable method to detect OSA risk. For children, only information on questionnaires and anatomical factors was found; two questionnaires accurately identified potential OSA risk cases. Anatomical factors also displayed a significant relation with OSA for both populations. CONCLUSION: Dentists have a fundamental role in early detection of potential OSA cases since they can use the methods identified in this review to perform an initial screening of the population. ABBREVIATIONS: OSA: Obstructive sleep apnea; PSG: Polysomnography; HST: Home sleep study; BMI: Body mass index; PPV: Positive predictive value; NPV: Negative predictive value; AHI: Apnea hypopnea index; RDI: Respiratory disturbance index; ODI: Oxygen desaturation index; PSQ: Pediatric Sleep Questionnaire; SRBD: Sleep-related breathing disorder; CSHQ: Children's Sleep Habits Questionnaire; ESS: Epworth Sleepiness Scale; PSQI: Pittsburgh Sleep Quality Index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".