The Effects of Music-Based Patterned Sensory Enhancement on Motor Function: A Scoping Review
Bibliographic record
Abstract
Background/Objectives: Patterned Sensory Enhancement (PSE), a Neurologic Music Therapy technique, utilizes rhythm and other musical elements to facilitate functional movement in diverse clinical populations. This scoping review is the first to systematically synthesize the current evidence surrounding PSE’s use and its effects on motor function across various populations in order to evaluate its therapeutic potential, identify gaps in the existing literature, and guide future research efforts. Methods: A literature search was conducted across five major databases (MEDLINE, Embase, PsycINFO, CINAHL, and Scopus) in accordance with the PRISMA-ScR guidelines. Results: From 1018 screened articles, 15 met the inclusion criteria. PSE has been demonstrated as effective across clinical populations, including Cerebral Palsy, stroke, Parkinson’s Disease, and psychiatric conditions. However, the results for studies on geriatric populations remain inconsistent. Despite the variability in the outcome measures and movement types assessed, PSE is consistently supported as an effective approach for enhancing motor function. However, to date, only a small number of studies across populations have been conducted. Conclusions: This scoping review suggests that PSE holds significant potential for improving motor function across a range of clinical populations. Further research is needed to explore the long-term effects, use standardized terminology, and identify the optimal implementation strategies tailored to the unique needs of different populations to maximize its therapeutic benefit.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".