Innovative Technology for the Assisted Delivery of Intensive Voice Treatment (LSVT<sup>®</sup>LOUD) for Parkinson Disease
Bibliographic record
Abstract
PURPOSE: To assess the feasibility and effectiveness of a newly developed assistive technology system, Lee Silverman Voice Treatment Companion (LSVT(®) Companion™, hereafter referred to as "Companion"), to support the delivery of LSVT(®)LOUD, an efficacious speech intervention for individuals with Parkinson disease (PD). METHOD: Sixteen individuals with PD were randomized to an immediate (n = 8) or a delayed (n = 8) treatment group. They participated in 9 LSVT LOUD sessions and 7 Companion sessions, independently administered at home. Acoustic, listener perception, and voice and speech rating data were obtained immediately before (pre), immediately after (post), and at 6 months post treatment (follow-up). System usability ratings were collected immediately post treatment. Changes in vocal sound pressure level were compared to data from a historical treatment group of individuals with PD treated with standard, in-person LSVT LOUD. RESULTS: All 16 participants were able to independently use the Companion. These individuals had therapeutic gains in sound pressure level, pre to post and pre to follow-up, similar to those of the historical treatment group. CONCLUSIONS: This study supports the use of the Companion as an aid in treatment of hypokinetic dysarthria in individuals with PD. Advantages and disadvantages of the Companion, as well as limitations of the present study and directions for future studies, are discussed.
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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) | 0.000 | 0.000 |
| 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) | 0.003 | 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".