The effect of biopsychosocial holistic care models on the cognitive function and quality of life of elderly patients with mild cognitive impairment: a randomized trial
Bibliographic record
Abstract
BACKGROUND: Cognitive dysfunction is a functional disorder that occurs after brain tissue damage, which can be classified as mild, moderate, or severe according to the degree of illness, especially in the elderly. Mild cognitive impairment (MCI) has many causes and is difficult to treat. It has been reported that biopsychosocial holistic care models can achieve good results in treating MCI. This study aimed to explore the application effect of biopsychosocial holistic care models on elderly patients with MCI. METHODS: A total of 140 patients with MCI diagnosed in Nantong People's Hospital (Nantong, China) from March 2019 to March 2020 were selected as the research cohort. Using a computer-generated randomization list, the participants were randomly allocated to either the observation group or control group, with 70 cases in each group. We compared the cognitive function and quality of life scores of the 2 groups before treatment, 1 month after treatment, and 3 months after treatment. RESULTS: In the first and third months after the intervention, the mini mental state examination (MMSE) score and Montreal Cognitive Assessment (MoCA) score of the observation group were higher than those of the control group, and activities of daily living (ADL) score was lower than that of the control group. The difference between MMSE and MoCA scores between the 2 groups of participants at the third month of treatment was statistically significant (P=0.000). CONCLUSIONS: Biopsychosocial holistic care models can improve the cognitive function and quality of life of elderly MCI patients. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2100046021.
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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 | 0.002 | 0.007 |
| 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 | 0.002 |
| 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".