Effects of Continuous Positive Airway Pressure on Neuroimaging Biomarkers and Cognition in Adult Obstructive Sleep Apnea: A Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Rationale Obstructive sleep apnea (OSA) is associated with cognitive impairment. The effects of continuous positive airway pressure (CPAP) on neuroimaging biomarkers and cognitive performance among middle-aged patients with OSA and normal cognition remain unclear. Objectives To investigate the effects of CPAP therapy over 12 months on neuroimaging biomarkers and cognitive performance. Methods In this multicenter, randomized clinical trial, we randomly assigned 148 participants with normal cognition and an apnea–hypopnea index ⩾15/h into two groups: patients receiving CPAP with best supportive care (BSC); and patients receiving BSC alone. The primary endpoint was Montreal Cognitive Assessment (MoCA) score at 6 months after enrollment. The secondary endpoints were intranetwork functional connectivity (FC) of default mode network (DMN) and cortical thickness assessed by functional and structural magnetic resonance imaging, other neuroimaging biomarkers, and neurobehavioral tests. Measurements and Main Results Between 2017 and 2021, 148 patients were recruited from five hospitals. Linear mixed models showed that there was no significant difference in MoCA scores at 6 months between the CPAP and BSC groups (difference, −0.04; 95% confidence interval [CI], −0.72 to 0.65; P = 0.91). However, there were significant differences in the FC of DMN (difference, −13.73; 95% CI, −23.40 to −4.06; P = 0.01) and cortical thickness (difference, −0.06 mm; 95% CI, −0.10 to −0.01 mm; P = 0.02) between CPAP and BSC groups at 6 months after treatment. No serious adverse events occurred. Conclusions CPAP improved cortical thickness and FC of DMN, suggesting that patients with OSA may recover from brain atrophic processes after CPAP treatment. However, no improvement in MoCA was found. Clinical trial registered with www.clinicaltrials.gov (NCT02886156).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".