‘O Sleep, O Gentle Sleep!’ - Profiling Sleep Quality in Patients Attending a Specialist Memory Clinic
Bibliographic record
Abstract
Abstract Background The importance of considering sleep in comprehensive assessment of cognition is well established. Objective measurement of sleep and incorporation of informant perceptions of sleep serve to enhance our understanding of the true quality of sleep and its potential contribution to cognitive wellbeing and brain health. Methods Retrospective review of data (n=60) from patients attending a regional specialist memory service, using multidisciplinary team meeting notes. Patients were included where a final diagnosis was available via Synergy electronic records and both a self-rated Pittsburgh Sleep Quality Index (PSQI) and informant-rated Cambridge Behavioural Inventory Revised (CBI- R) forms were available. Data were anonymised and analysed using Microsoft Excel. Results Median age 68 years (42-85); M: F- 31:29; Diagnostic breakdown: AD (30%), MCI (28%), AD/V (3%), SMC (15%), FTD (11%), DLB (7%), VaD (2%), VCI (2%) and Korsakoff (2%). Sleep was unobserved in 45% of cases (n=27). CBI-R suggested sleep disturbance in 73% (n=43) and daytime somnolence 58% (n=35); Median PSQI score: 6/21. Median time to get to sleep was 15 mins and actual sleep 7 hrs. Disturbances to sleep: Pain 13% (n=12), bad dreams 15% (n=14) cough or loud snoring 18% (n=16), trouble breathing 9% (n=8) and mostly high frequency toilet use 45% (n=41). 23% (n=14) took night medication and 12/14 medicated 3 or more times weekly. Self-rating: ‘Very good’ (32%) ‘Fairly good’ (40%) ‘Fairly bad’ (20%) ’Very bad’ (8%); Enthusiasm levels were problematic in 67% (n=40), 18% (n=11) had trouble staying awake. Conclusion Self-reported sleep quality in this cohort scored well/low on global PSQI but informant history suggested it may be poorer. The prevalence of daytime somnolence may be an important consideration in diagnostic clarification. Patient profiling of factors e.g. high frequency toileting and reduced enthusiasm could provide potential roadmaps to facilitate the personalisation of sleep and optimisation of brain health and wellbeing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".