Predictors of Sleep-Disordered Breathing in Community-Dwelling Adults<subtitle>The Sleep Heart Health Study</subtitle>
Bibliographic record
Abstract
BACKGROUND: Sleep-disordered breathing (SDB) is common, but largely undiagnosed in the general population. Information on demographic patterns of SDB occurrence and its predictive factors in the general population is needed to target high-risk groups that may benefit from diagnosis. METHODS: The sample comprised 5615 community-dwelling men and women aged between 40 and 98 years who were enrolled in the Sleep Heart Health Study. Data were collected by questionnaire, clinical examinations, and in-home polysomnography. Sleep-disordered breathing status was based on the average number of apnea and hypopnea episodes per hour of sleep (apnea-hypopnea index [AHI]). We used multiple logistic regression modeling to estimate cross-sectional associations of selected participant characteristics with SDB defined by an AHI of 15 or greater. RESULTS: Male sex, age, body mass index, neck girth, snoring, and repeated breathing pause frequency were independent, significant correlates of an AHI of 15 or greater. People reporting habitual snoring, loud snoring, and frequent breathing pauses were 3 to 4 times more likely to have an AHI of 15 or greater vs an AHI less than 15, but there were weaker associations for other factors with an AHI of 15 or greater. The odds ratios (95% confidence interval) for an AHI of 15 or greater vs an AHI less than 15 were 1.6 and 1.5, respectively, for 1-SD increments in body mass index and neck girth. As age increased, the magnitude of associations for SDB and body habitus, snoring, and breathing pauses decreased. CONCLUSIONS: A significant proportion of occult SDB in the general population would be missed if screening or case finding were based solely on increased body habitus or male sex. Breathing pauses and obesity may be particularly insensitive for identifying SDB in older people. A better understanding of predictive factors for SDB, particularly in older adults, is needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".