Impact of Music Therapy on Cognitive Function in Elderly Patients with Mild Cognitive Impairment and Early Dementia: A Systematic
Bibliographic record
Abstract
Introduction: Cognitive decline in the elderly significantly impacts the quality of life of patients and caregivers. Safer non-pharmacological interventions that stimulate natural healing have gained popularity recently. This systematic review evaluates the impact of music therapy (MT) in improving cognition and neuropsychiatric symptoms in elderly with mild cognitive impairment (MCI) and early dementia. Methods: Studies included randomized controlled trials (RCTs) and quasi-experimental studies focusing on MT for elderly patients (age ≥65 years) with MCI or early dementia due to Alzheimer's disease (AD) or vascular dementia (VD). Studies without control groups, non-peer-reviewed and not in English were excluded. Searches were run in PubMed, CENTRAL, Scopus, and Web of Science using key concepts dementia, music therapy and cognition. The Cochrane Risk of Bias Tool was used to evaluate the risk of bias in the RCTs. Interventions included any modality of MT. Outcomes evaluated cognition, using valid and reliable scales. Results: Fourteen studies (1,169 participants) were included. Significant post-intervention improvements were noted in cognitive measures such as the mini-mental state exam, Montreal cognitive assessment, and Digit Span scores. Active MT reduced neuropsychiatric symptoms in AD as measured by neuropsychiatric inventory scores. However, the effects varied across cognitive domains and MT modalities. The limitations of the studies included small sample sizes, short intervention and follow-up durations, and heterogeneous study designs limiting generalizability. Conclusions: Music therapy showed potential as a non-pharmacological intervention to improve cognitive function and reduce neuropsychiatric symptoms in elderly patients with MCI and early dementia. Further large-scale, well-designed studies are needed.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".