3R Nursing Combined with Dietary and Nutritional Interventions Enhances Self-care Ability in Elderly Patients with Vascular Dementia
Bibliographic record
Abstract
BACKGROUND: Vascular dementia (VD) is an extremely common neurological dysfunction in the elderly population, and greatly affects the patient's ability to take care of themselves. Recent research suggests that VD patients need more targeted and individualized nursing during treatment, so as to enhance cognitive function and therapeutic efficacy. The objective of this study is to observe the effect of reminiscence, reality, and remotivation (3R) nursing combined with dietary and nutritional interventions on elderly patients with VD, so as to provide clinical evidence for the management of VD in older adults. METHODS: 120 elderly VD patients admitted between December 2022 and December 2023 were selected, including 64 cases receiving 3R nursing combined with dietary and nutritional interventions (the research group) and 56 cases receiving routine nursing (the control group). The two groups were compared in terms of neurological function, self-care ability, and nutritional status before and after nursing, as well as nursing compliance. After the completion of the care, patients' quality of life and family satisfaction were investigated. RESULTS: In comparison with the control group, the research group displayed higher scores on the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), greater self-care ability, and higher levels of nutritional proteins and grip strength (p < 0.05). In addition, patients in the research group displayed greater nursing compliance and quality of life of patients, as well as higher family satisfaction (p < 0.05). CONCLUSIONS: 3R nursing combined with dietary and nutritional interventions can effectively improve the neurological function of VD patients and enhance their self-care ability.
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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 | 0.000 | 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) | 0.001 | 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".