Cognitive Impairment and Related Factors Among Middle-Aged and Elderly Patients with Type 2 Diabetes from a Bio-Psycho-Social Perspective
Bibliographic record
Abstract
OBJECTIVE: This study investigated the biomedical, psychological, and social behavior risk factors for cognitive impairment in middle-aged and elderly patients with type 2 diabetes mellitus (T2DM). METHODS: This cross-sectional study included 240 patients with T2DM. A questionnaire was used to collect demographic and disease-related data on patients, and the Self-rating Depression Scale (SDS), Diabetes Self-care Scale (DSCS), and Social Support Rating Scale (SSRS) were used to assess patients' depression status, self-management behavior, and social support, respectively. The Chinese version of the Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive function, with a score <26 set as the threshold for cognitive impairment. RESULTS: The prevalence of cognitive dysfunction in middle-aged and elderly patients with T2DM was 52.5%. Multivariate logistic regression analysis showed that older age, a history of hypoglycemia within 1 month, and depression were independent risk factors for cognitive impairment. Education for >12 years, urban living, and a higher total score on the DSCS were independent protective factors against cognitive impairment. CONCLUSION: T2DM patients with high risk of cognitive impairment can be identified early from the bio-psycho-social perspective. Patients with T2DM who are older, less educated, living in rural areas, have hypoglycemia history, and have poor self-management of diabetes are at increased risk of cognitive impairment. Closer monitoring of patients with hypoglycemia, early detection of depression, and improving patients' self-management capacity can prevent cognitive impairment in middle-aged and elderly patients with T2DM.
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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) | 0.001 | 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".