O014 Feasibility of evaluating early markers of neurodegeneration in a sleep clinic setting
Bibliographic record
Abstract
Abstract Introduction Obstructive sleep apnea (OSA) is an emerging risk factor for cognitive decline. Sleep clinics are well positioned to screen for early biomarkers. We will evaluate the feasibility of cognitive testing and neurodegenerative blood-biomarker collection in a sleep clinic. Methods Males and females (>40 years) referred for polysomnography on suspicion of OSA, who consent to be contacted regarding research were invited to participate. Exclusions included existing OSA or any neurological condition. Participants underwent in-laboratory overnight polysomnography and questionnaires, with additional blood sampling and abbreviated neuropsychological testing. Results Over 16 months of recruitment, 968 patients >40 were referred for polysomnography. Of 73 contactable patients who met all eligibility criteria, 59 (80.8%) stated willingness to participate. The only reason for non-participation was unavailability for morning testing (n=14, 19.2%). In preliminary analysis, 57 participants (female=17, age 56.0±10.2) had completed testing. Moderate-severe OSA (AHI>15) was diagnosed in 24 (42.1%) participants (median(IQR) 13.3 (7.6-32.3). 10 (18%) participants showed mild impairment on the Montreal Cognitive Assessment (MoCA) (score<26) (mean± SD 27.3±1.9). The test of premorbid functioning (TOPF) was high (116±8.8). Discussion Over 80% of patients were receptive to additional brain health screening during routine sleep assessments. There were fewer instances of mild impairment compared with recent literature (Beaudin 2021). The TOPF scores indicate high premorbid functioning that may be protective. Data collection will be continued including 12-month follow-up. Future analyses will explore quantitative sleep metrics, further cognitive domains and blood-based biomarkers of neurodegeneration which may detect early changes in this representative sleep clinic cohort.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".