Prevalence of High Epworth Sleepiness Scale Scores in a Rural Population
Bibliographic record
Abstract
BACKGROUND: Increased daytime sleepiness is an important symptom of obstructive sleep apnea (OSA). OSA is frequently underdiagnosed, and the Epworth Sleepiness Scale (ESS) can be a useful tool in alerting physicians to a potential problem involving OSA. OBJECTIVE: To measure the prevalence and determinants of daytime sleepiness measured using the ESS in a rural community population. METHODS: A community survey was conducted to examine the risk factors associated with ESS in a rural population in 154 households comprising 283 adults. Questionnaire information was obtained regarding physical factors, social factors, general medical history, family medical history, ESS score, and self-reported height and weight. Multivariable binary logistic regression analysis based on the generalized estimating equations approach to account for clustering within households was used to predict the relationship between a binary ESS score outcome (normal or abnormal) and a set of explanatory variables. RESULTS: The population included 140 men (49.5%) and 143 women (50.5%) with an age range of 18 to 97 years (mean [± SD] 52.0±14.9 years). The data showed that 79.2% of the study participants had an ESS score in the normal range (0 to 10) and 20.8% had an ESS score >10, which is considered to be abnormal or high sleepiness. Multivariable regression analysis revealed that obesity was significantly associated with an abnormal or high sleepiness score on the ESS (OR 3.40 [95% CI 1.31 to 8.80). CONCLUSION: High levels of sleepiness in this population were common. Obesity was an important risk factor for high ESS score.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".