Sleep efficiency in patients with Parkinson’s disease with cognitive impairment
Bibliographic record
Abstract
Objective To assess the sleep quality in the Parkinson’s disease (PD) patients with cognitive impairment and to analyze the possible relationship between sleep disorders and cognitive impairment in PD patients. Methods One hundred and nine PD patients were assessed cognitive function using Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Wechsler Intelligence Scale and Wechsler Memory Scale, and sleep quality was evaluated using the Parkinson’s Disease Sleep Scale-2 (PDSS-2), Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale. All PD patients were divided into abnormal cognitive group (n=46) and normal cognitive group (n=63). Results PDSS-2 and PSQI scores were significantly higher in abnormal cognitive group than in normal cognitive group (23.88±13.00 vs 13.80±9.76, t=-3.745, P<0.01; 10.58±4.89 vs 6.87±4.64, t=-3.269, P=0.001). The scores of PDSS-2 in PDSS-2-2, 3, 4, 5, 6, 9 and 〖JP2〗PSQI in sleep quality (1.76±1.00), sleep time (1.42±1.09), sleep efficiency (1.52±1.28) as well as sleep disorder (1.61±〖JP〗0.56) were also significantly higher in abnormal cognitive group than in normal cognitive group (1.04±0.85, 0.91±1.08, 0.89±1.17, 1.25±0.65, t=-3.134, -2.286, -2.363, -2.590, all P<0.05). The correlations between the scores of MoCA and PDSS-2, MoCA and PSQI were significant (r=-0.24, P=0.03; r=-0.23, P=0.04). Conclusions The PD patients with abnormal cognition have lower sleep quality than those with normal cognition. Combining PDSS-2 with PSQI could preliminarily understand the underlying causes and the degree of PD patients with sleep disorders. Key words: Parkinson disease; Cognition disorders; Sleep disorders; Sleep
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".