Survey of cognitive dysfunction and influencing factors in elderly inpatients in geriatric department of general hospital
Bibliographic record
Abstract
Objective To investigate the cognitive dysfunction of inpatients in geriatric department general hospital and to analyze the influencing factors. Methods A total of 205 patients hospitalized in Peking University Third Hospital were evaluated by mini-mental state examination(MMSE). All patients were divided into cognitive impairment group and non-cognitive impairment group.The general characteristics, prevalence, biochemistry indexes, comorbidities, and color ultrasound-detected plaque and stenosis in carotid arteries and artery of lower extremity were analyzed and compared between two groups.The logistic multiple regression analysis was used to explore the influencing factors of cognitive dysfunction. Results The differences in age(P=0.027), education(P=0.003), marital status(P=0.000), living situation(P=0.001), hypertension history(P=0.031), type 2 diabetes mellitus(T2DM)history(P=0.036), cerebrovascular disease history(P=0.043)and HbA1c(P=0.032)were statistically significant between cognitive impairment and non-cognitive impairment group(all P<0.05). Logistic multiple regression analysis showed that age, marital status, T2DM history, cerebrovascular disease history and comorbidities were positively correlated with cognitive impairment(all P<0.05). Education was negatively correlated with cognitive impairment(P<0.05). Conclusions Cognitive impairment is associated with age, education, marital status, T2DM history, cerebrovascular disease history and comorbidities. Key words: Cognition disorders; Risk factors
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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 | 0.001 |
| 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".