Efficacy of video-music therapy on quality of life improvement in a group of patients with Alzheimer's disease: a pre-post study.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: Alzheimer's disease is the most common degenerative dementia with a predominantly senile onset. The difficult management of altered behaviour related to this disorder, poorly responsive to pharmacological treatments, has stimulated growth in non-pharmacological interventions, such as music therapy, whose effectiveness has not been supported by the literature up to now. The aim of this study was to evaluate the efficacy of video-music therapy on quality of life improvement in Patients affected by Alzheimer's Disease (AD). METHODS: A pre-post study was conducted in a residential facility. 32 AD Patients, who attended this facility daily to participate in supportive and rehabilitative programs, were treated with 2 cycles of 6 video-music-therapy sessions, which consisted of folk music and video, recalling local traditions. In order to investigate their cognitive status, Mini Mental State Examination (MMSE) was administered and Patients were divided into stages according to MMSE scores. After each session of video-music-therapy, Quality of Life in Alzheimer's Disease Scale (QOL-AD) was administered to our Patients. RESULTS: 21 AD Patients completed the 2 cycles of video-music therapy. Among them, only the Patients with questionable, mild and moderate neurocognitive impairment (MMSE Stages 1, 2, 3) reported an improvement in their quality of life, whereas the Patients with severe deterioration (MMSE stage 4) did not report any change. Many items of QOL-AD improved, showing a statistically significantly correlation to each other. CONCLUSIONS: Video-music therapy was a valuable tool for improving the quality of life only in Patients affected by less severe neurocognitive impairment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".