Respiratory event-related physiological biomarkers and cognitive performance in obstructive sleep apnoea
Bibliographic record
Abstract
BACKGROUND: respiratory event-related electroencephalography (EEG) activity and autonomic responses) and the risk of cognitive impairment. METHODS: ) from the Canadian Sleep and Circadian Network observational cohort were studied. Brain Response to Event (BReTE) was derived from EEG power (defined as mean (median post-event power/median pre-event power) (frequency range: 0.5-50 Hz)) for each individual. Event-related autonomic responses were measured by heart rate response to events (ΔHR: the difference between maximum post-event heart rate and minimum heart rate during event) and photoplethysmography (PPG)-derived vasoconstriction activity (event-related area and depth of PPG decline). Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA), Wechsler Digit Symbol Coding (DSC) and Rey Auditory Verbal Learning Test-Delayed Recall (RAVLT-DR). Multiple logistic regression examined the independent associations between biomarkers and outcomes. RESULTS: We studied 537 individuals (42% female) with a median age of 55 years. In fully adjusted models, each 1sd decrease in BReTE was associated with higher odds of poor cognitive performance indicated by MoCA <26 (OR 1.42, 95% CI 1.13-1.79; p=0.003), DSC <25th percentile (OR 1.35, 95% CI 1.02-1.84; p=0.04) and RAVLT-DR <25th percentile (OR 1.50, 95% CI 1.13-2.02; p=0.007). Additionally, those with low ΔHR compared to the mid-range group were at increased risk of poor cognitive performance. Vasoconstriction indices were not associated with cognitive performance. CONCLUSION: Blunted EEG and heart rate responses to respiratory events are linked to poorer cognitive performance in OSA, highlighting the value of EEG in identifying individuals at risk for cognitive impairment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".