0422 Correlates Of Cognitive Function In Patients With Insomnia Disorders: A Cross-sectional Study
Bibliographic record
Abstract
It has been widely accepted that insomnia can significantly impair cognitive function. However, there is very limited evidence regarding risk factors for these cognitive impairments. Hence, this study aims to explore the risk factors for cognitive impairment in patients with insomnia. 129 patients [mean age: 48.8 ± 11.4 years; 89 (69.0%) females] with insomnia disorder were recruited. Insomnia Severity Index (ISI) was used to measure the severity of insomnia symptoms. One-week sleep diary was used to assess sleep patterns, including the sleep onset latency, sleep efficiency, total sleep and number of awakening after sleep. The Montreal Cognitive Assessment (MoCA) was used to measure the cognitive function. Risk factors were identified by using the multivariate linear regression with stepwise variable selection method. MoCA score was negatively correlated with age (r = -3.2; P < 0.01), sleep onset latency (r = -3.2; P < 0.01) while positive correlated with education level (r = 0.50; P < 0.01), sleep efficiency (r = 0.26; P < 0.01) and sleep duration (r = 0.21; P = 0.02). However, MoCA was not associated with the ISI total score (r = -0.09; P = 0.30). In linear regression model, MoCA score was only associated with sleep efficient (regression coefficient = 2.70; P = 0.04) after controlling for education level by using stepwise approach, which included age, sex, education level, and other parameters with significant correlation with MoCA. Sleep quality as measured by sleep efficiency in sleep diary seems to be correlated with cognitive function in patients with insomnia disorder. However, severity of insomnia as measured by the ISI is not likely to be correlated with cognitive function in patients with insomnia disorder. National Natural Science Foundation of China(NSFC81870077)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".