The effect of SpeechVive device on speech intelligibility in Parkinson’s Disease patients: Randomized Clinical Trial Protocol
Bibliographic record
Abstract
Background: Hypokinetic dysarthria -with hypophonia as its main symptom- is a common feature of Parkinson’s disease, affecting approximately 90% of patients. Hypophonia, characterized by reduced speech volume, leads to difficulties in communication with others due to decreased speech intelligibility. Current treatments involve intensive and cognitively demanding behavioral therapies such as the Lee Silverman Voice Treatment (LSVT). The SpeechVive is a wearable device that produces noise to elicit increased vocal intensity utilizing a natural reflex through the Lombard effect.Methods: We propose a multicenter, phase III, two-armed, parallel, open-label, randomized controlled trial comparing the effectiveness of LSVT with SpeechVive. We seek to assign 238 patients to either LSVT or SpeechVive device in a 1:1 ratio through a stratified permuted block randomization. Patients ages 50 to 80 years, diagnosed with idiopathic Parkinson's Disease based on MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) criteria, a Hoehn and Yahr stage 2 and 3, on stable dopaminergic doses for the past 3 months, with perceived communication difficulties will be included in the trial. Patients will be excluded if they present additional neurodegenerative diseases, prior stroke, laryngeal pathologies, hearing or a severe visual impairment, or who underwent speech therapy or have a deep brain stimulation electrode implanted. The primary outcome is speech intelligibility measured through the Speech Intelligibility Test (SIT) for windows. Secondary outcomes include adherence, the vocal intensity measured with Sound Pressure Level (SPL), Vocal Handicap Index (VIH), and Parkinson's Disease Questionary-39. We will measure each outcome at baseline and after eight weeks of treatment. Our principal statistical analysis is multiple linear regression analysis, with age, gender, site, and PD severity as covariates.Discussion: We present a protocol for a randomized controlled trial addressing an important issue that hampers the ability of Parkinson's Disease patients to communicate effectively. We aim at exploring SpeechVive as an alternative, more accessible treatment for hypophonia in patients with Parkinson’s Disease. Keywords: SpeechVive device, Lee Silverman Speech Therapy (LSVT), hypophonia, Parkinson's Disease, the Lombard effect, Speech intelligibility.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".