Exploring the effects of combined nostalgic activities and music therapy on Alzheimer's disease outcomes
Bibliographic record
Abstract
Objective Exploring the effects of combination of nostalgic activity-based therapies, including music therapy on cognitive function, negative emotions, and sleep quality in patients with mild to moderate Alzheimer's disease. Methods A total of 63 patients with mild to moderate Alzheimer's disease who were treated at the Sichuan Provincial Psychiatric Center of the People's Hospital of Sichuan Province from January to June 2023 were selected as the research subjects. They were randomly divided into a study group (n = 31) and a control group (n = 32) using a random number table method. The control group received routine treatment and nursing care, while the study group received nostalgic music therapy intervention on the basis of the control group. The Mini Mental State Examination (MMSE), Montreal Cognitive Assessment Scale (MOCA), Self Rating Anxiety and Depression Scale (SAS, SDS), and Pittsburgh Sleep Quality Index (PSQI) of the two groups were compared. Results A total of 30 cases from each group completed the study. After 12 weeks of intervention, the MMSE and MOCA scores of both groups of patients increased, and the treatment group was higher than the control group (P < 0.05); SAS, SDS and PSQI scores decreased compared with those before intervention, and the treatment group was lower than the control group (P < 0.05). Conclusion Nostalgic music therapy can improve cognitive function, alleviate negative emotions, and improve sleep quality in patients with mild to moderate Alzheimer's disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".