The Science and Practice of LSVT/LOUD: Neural Plasticity-Principled Approach to Treating Individuals with Parkinson Disease and Other Neurological Disorders
Bibliographic record
Abstract
Our 15 years of research have generated the first short- and long-term efficacy data for speech treatment (Lee Silverman Voice Treatment; LSVT/LOUD) in Parkinson's disease. We have learned that training the single motor control parameter amplitude (vocal loudness) and recalibration of self-perception of vocal loudness are fundamental elements underlying treatment success. This training requires intensive, high-effort exercise combined with a single, functionally relevant target (loudness) taught across simple to complex speech tasks. We have documented that training vocal loudness results in distributed effects of improved articulation, facial expression, and swallowing. Furthermore, positive effects of LSVT/LOUD have been documented in disorders other than Parkinson's disease (stroke, cerebral palsy). The purpose of this article is to elucidate the potential of a single target in treatment to encourage cross-system improvements across seemingly diverse motor systems and to discuss key elements in mode of delivery of treatment that are consistent with principles of neural plasticity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".