Prevalence and Associated Factors of Obstructive Sleep Apnea in Saudi Arabia: A Web Based Questionnaire Based Study
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is a common condition that is prevalent among males. Though less is known about its prevalence among females. Furthermore, STOP-BANG score test is a self-reported survey that is widely used for diagnosing risks of obstructive sleep apnea. Due to the lack of determining the prevalence of OSA among females and its risk factors using STOP-BANG score test, the study was brought off. METHODS: A web-based cross-sectional study was conducted using modified STOP-BANG questionnaire that was distributed through WhatsApp, Twitter, Snapchat, and Telegram to determine the prevalence and associated factors of OSA among Saudi females in comparison to males. RESULTS: A total of 1377 participants completed the survey, total of 819 (59.4%) were females. The results showed that Prevalence of OSA among females is much less and the number of who had whether mild or moderate risk of OSA was 44 (3.9%) while 78 (14%) were males. Among the survey variables, the only significant factors were Smoking, Snoring, Body mass index, and tiredness. CONCLUSION: OSA is a common sleeping disorder among men. Contrastingly, its prevalence is much less among females. STOP-BANG score is a good, cheap, and easy to use for diagnosing risk of OSA. Finally, OSA is associated with smoking, BMI, tiredness, and snoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".