The relationship between sleep disturbance and cognitive impairment: A population‐based cross‐sectional study
Bibliographic record
Abstract
Abstract Background Growing evidence suggest that sleep disturbance is a risk factor for Alzheimer’s disease, but the relationship between sleep disturbance and cognitive impairment is not clear. Method This was a population‐based cross‐sectional study. A total of 1649 participants from a village in the suburbs of Xi’an, China were enrolled from January 3 to March 26, 2017. Sleep quality was assessed using the Pittsburgh sleep quality index (PSQI). Cognitive function was assessed with the mini‐mental state examination (MMSE). Univariate and multivariate analyses were used to analyze the relationships between sleep disturbance and cognitive impairment. Result Among 1649 subjects,110(6.7%) were diagnosed as cognitive impairment, and 960(58.2%) were diagnosed as sleep disturbance. In bivariate analysis, cognitive impairment was associated with insomnia)ρ=0.110; 95%CI=0.060‐0.154 ; P<0.01(, sleep efficiency)ρ=‐0.152; 95%CI=‐0.196‐ ‐0.105; P<0.01(, age)ρ=0.237; 95%CI=0.195‐0.279; P<0.01(and educational level )ρ=‐0.190; 95%CI=‐0.229‐ ‐0.149; P<0.01(. In the binary logistic regression, cognitive impairment was positively associated with the scales of PQSI (OR=1.072; 95%CI=1.016‐1.130; P=0.012). In the internal constitution of PQSI, cognitive impairment was positively associated with the sleep duration (OR=1.567; 95% CI=1.242‐1.979; P<0.01), step disturbances (OR=0.003; 95%CI=0.000‐0.023; P=0.017), and negatively associated with the habitual sleep efficiency (OR=1.068; 95%CI=1.012‐1.127; P<0.01). Cognitive impairment also was negatively associated with patients age (OR=0.130; 95%CI=0.069‐0.244; P<0.01). Conclusion Sleep disturbances is associated with cognitive impairment. However, the causal relationships between sleep disturbances and cognitive impairment are not clear and need to be further studied.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".