Comparison of Sleep Apnea Questionnaires and Reported Diagnosis in Neurological Disorders of Aging
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is associated with worse outcomes in stroke, Alzheimer's disease (AD) and Parkinson's disease (PD), but diagnosis is challenging in these groups. We aimed to compare the prevalence of high risk of OSA based on commonly used questionnaires and self-reported OSA diagnosis: 1. within groups with stroke, AD, PD and the general population (GP); 2. Between neurological groups and GP. METHODS: Individuals with stroke, PD and AD were identified in the Canadian Longitudinal Study of Aging (CLSA) by survey. STOP, STOP-BAG, STOP-B28 and GOAL screening tools and OSA self-report were compared by the Chi-squared test. Logistic regression was used to compare high risk/self-report of OSA, in neurological conditions vs. GP, adjusted for confounders. RESULTS: = 125). In all groups, a positive GOAL was the most prevalent, while positive STOP was least prevalent among questionnaires. Significant variations in high-risk OSA were observed between different questionnaires across all groups. Under 1.5% of individuals self-reported OSA. While all questionnaires suggested a higher prevalence of OSA in stroke than the GP, for PD and AD, there was heterogeneity depending on questionnaire. CONCLUSIONS: The wide range of prevalences of high risk of OSA resulting from commonly used screening tools underscores the importance of validating them in older adults with neurological disorders. OSA was self-reported in disproportionately small numbers across groups, suggesting that OSA is underdiagnosed in older adults or underreported by patients, which is concerning given its increasingly recognized impact on brain health.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".