P4‐403: ENHANCING COGNITION BY USING LOW‐COST IN‐HOME PORTABLE SLEEP EQUIPMENT: A FEASIBILITY STUDY
Bibliographic record
Abstract
Obstructive sleep apnea (OSA), which causes pauses in breathing during sleep, increases the risk of developing dementia. In-laboratory polysomnography (PSG) is the gold standard tool to diagnose OSA, but few patients are screened by PSG due to patient refusal to spend a night in a sleep laboratory, high costs, and lengthy wait times. Home sleep apnea testing (HSAT) may be a more accessible alternative, as it is simple to use, conveniently administered in a patient's own home and validated against PSG. We assessed if HSAT is a clinically feasible approach to screen for OSA in a cognitively impaired patient population. Patients with cognitive impairment due to neurodegenerative and/or vascular etiologies were enrolled and completed various cognitive, sleep, and mood assessments and questionnaires. Patients also completed OSA screening using the ApneaLink Air HSAT. The HSAT will be considered a feasible technique if ≥80% of the study population obtain ≥4 hours of analyzable data. To date, sixty-three patients have completed baseline testing and attempted HSAT. The mean age is 72.37±11.29 years, 47.6% are male and the median Montreal Cognitive Assessment score is 22. Fifty-nine participants (93.7%) obtained ≥4 hours of analyzable data using the HSAT. Our preliminary analysis demonstrates HSAT is feasible in a cognitively impaired patient population. As OSA is a modifiable risk factor for patients with dementia, HSAT has the potential to lead to expedited treatment for OSA, which may potentially improve health related outcomes such as cognition.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".