Predictors of Sleep Apnea in the Canadian Population
Bibliographic record
Abstract
Older age, obesity, hypertension, snoring, and excessive daytime sleepiness have been associated with sleep apnea. The objective of this study was to determine the prevalence (crude and adjusted), as well as the risk factors, of sleep apnea in the adult Canadian population. Data from the 2009 Sleep Apnea Rapid Response (SARR) questionnaire were used to identify the risk factors, and all sleep-related questions in the SARR questionnaire were used. The outcome variable of interest was health professional-diagnosed sleep apnea. Covariates of interest were demographic variables, population characteristics, respiratory and cardiovascular diseases, and enabling resources. The multiple logistic regression model adjusted for the clustering effect was used to analyze the data. Sleep apnea was diagnosed in 858,913 adults (3.4% of the population), and more men (65.4%) than women (34.6%) were diagnosed with sleep apnea. Multivariable logistic regression analysis indicated that age (45 and older), loud snoring, sudden awakening with gasping/choking (rare/sometimes and once or more a week), and nodding off/falling asleep in driving in the past 12 months were significantly associated with diagnosed sleep apnea. Predictive probability demonstrated that in overweight and obese persons, ≥15 minutes of daily exercise significantly decreased the risk of diagnosed sleep apnea. The conclusion of this study is that in the Canadian population, sleep apnea is associated with older age, loud snoring, and sleeping problems. The protective effect of exercise warrants further investigation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".