Continuous Positive Airway Pressure and Progression of Enlarged Perivascular Spaces in Adults With Obstructive Sleep Apnea
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: MRI-visible brain perivascular spaces (PVSs) represent an imaging feature of cerebral small vessel disease that is associated with hypertension, diabetes, and poor sleep quality and correlated with cognitive impairment and dementia. Although research shows that sleep apnea and sleep disruption are associated with PVS volumes, few studies have investigated whether sleep apnea treatment can modify this association. We aimed to test the hypothesis that continuous positive airway pressure (CPAP) therapy can change the trajectory of PVS progression in adults with sleep apnea. METHODS: In this longitudinal observational treatment study, we recruited participants from sleep clinics at the Royal Infirmary of Edinburgh and Sunnybrook Health Sciences Centre with moderate-to-severe hypoxemic sleep apnea (apnea hypopnea index ≥15 and oxygen desaturation index ≥10). Before and after a minimum 4 months of CPAP, we obtained pulse oximetry (WatchPAT) and performed brain MRI, with quantification of PVS volumes in the basal ganglia (BG) and centrum semiovale (CSO). We used linear mixed-effects models to test for associations between CPAP usage, changes in PVS volumes, and baseline sleep apnea severity. RESULTS: = 0.008). DISCUSSION: Optimal CPAP use may moderate the effects of sleep apnea on trajectories of MRI-visible PVS volumes. Although the observational nonrandomized nature of this study is a limitation, these data suggest that CPAP may be an effective means of slowing PVS dysfunction progression, a key correlate of aging and dementia, in adults with sleep apnea. TRIAL REGISTRATION INFORMATION: NCT03410095. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that, in patients with moderate-to-severe sleep apnea, optimal adherence to CPAP treatment stabilizes the progression of BG PVS volume.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".