Developing a music-based digital therapeutic to help manage the neuropsychiatric symptoms of dementia
Bibliographic record
Abstract
The greying of the world is leading to a rapid acceleration in both the healthcare costs and caregiver burden that are associated with dementia. There is an urgent need to develop new, easily scalable modalities of support. This perspective paper presents the theoretical background, rationale, and development plans for a music-based digital therapeutic to manage the neuropsychiatric symptoms of dementia, particularly agitation and anxiety. We begin by presenting the findings of a survey we conducted with key opinion leaders. The findings highlight the value of a music-based digital therapeutic for treating neuropsychiatric symptoms, particularly agitation and anxiety. We then consider the neural substrates of these neuropsychiatric symptoms before going on to evaluate randomized control trials on the efficacy of music-based interventions in their treatment. Finally, we present our development plans for the adaptation of an existing music-based digital therapeutic that was previously shown to be efficacious in the treatment of adult anxiety symptoms.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".