Association Between Cognitive Function and Emotion, Sleep, Frailty, and Nutrition in Hospitalized Patients
Bibliographic record
Abstract
BACKGROUND: With the rapid increase in China's aging population, cognitive impairment in the elderly has become a significant public health issue. AIMS: In this study we performed a cross-sectional analysis to comprehensively investigate the relationship between cognitive function and emotion, sleep, frailty, nutrition, and clinical variables in hospitalized geriatric patients according to age group and sex. We determined the most important risk factors for cognitive impairment. METHOD: A total of 1121 inpatients were recruited from the Department of Gerontology at the Fifth Affiliated Hospital of Sun Yat-sen University, China, from August 2023 to April 2024. Cognitive assessment was performed using the Mini-Mental State Examination scale and Montreal Cognitive Assessment. The sleep quality was evaluated based on the Pittsburgh Sleep Quality Index, and anxiety and depression were evaluated based on the Hamilton Anxiety Scale and Hamilton Depression Scale. RESULTS: Sex and age differences existed with respect to cognition, emotion, and sleep quality. After full adjustment, age, education level, working status, hemoglobin level, activities of daily living, Hamilton Depression Scale, and Pittsburgh Sleep Quality Index scores were significantly and independently associated with cognitive impairment. DISCUSSION: Geriatric patients with a better mood, sleep and nutrition status, higher education level, and more social engagement performance had superior cognitive function. Interventions, such as valuing education, improving sleep, relaxing emotions, preventing anemia, and adjusting lifestyle, may help prevent the development of cognitive deficits. Elderly and female patients required special attention. CONCLUSIONS: Various factors were shown to contribute to maintenance of cognitive function.
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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 | 0.000 | 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".