Association between obstructive sleep apnea and persistent postural-perceptual dizziness
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) has been linked to vestibular dysfunction, but no prior studies have investigated the relationship between Persistent Postural-Perceptual Dizziness (PPPD), a common cause of chronic dizziness, and OSA. OBJECTIVE AND METHODS: We determined the frequency of OSA in an uncontrolled group of PPPD patients from a tertiary dizziness clinic based on polysomnogram (PSG). We then assessed the sensitivity and specificity of common OSA questionnaires in this population. RESULTS: Twenty-five patients with PPPD underwent PSG (mean age 47, 60% female, mean BMI 29.5). A majority, or 56%, of patients were diagnosed with OSA, and in most, the OSA was severe. OSA patients were older (56 years versus 40 years, p = 0.0006) and had higher BMI (32 versus 26, p = 0.0078), but there was no clear gender bias (56% versus 64% female, p = 1.00). The mean sensitivity and specificity of the STOP BANG questionnaire for detecting OSA was 86% and 55%, respectively. Sensitivity and specificity of the Berlin Questionnaire was 79% and 45%, respectively. CONCLUSIONS: The prevalence of OSA was much higher in our small PPPD group than in the general population. Screening questionnaires appear to demonstrate good sensitivity to detect PPPD patients at risk of OSA in this small study. Future studies should confirm these findings and determine whether treatment of OSA improves symptoms in PPPD.
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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 | 0.003 |
| 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 | 0.000 |
| 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".