Intensive Voice Treatment (Lee Silverman Voice Treatment [LSVT LOUD]) for Children With Down Syndrome: Phase I Outcomes
Bibliographic record
Abstract
PURPOSE: This study examined the effects of an intensive voice treatment Lee Silverman Voice Treatment (LSVT LOUD) on children with Down syndrome (DS) and motor speech disorders. METHOD: A Phase I, multiple baseline, single-subject design with replication across nine participants with DS was used. Single-word intelligibility, acoustic measures of vocal functioning, and parent perceptions of pre- and posttreatment communication function were used as treatment outcome measures. RESULTS: All participants completed the full dose of LSVT LOUD and showed gains on one or more of the outcome measures. Patterns of posttreatment improvements were not consistent across participants but were more frequently observed on trained maximum performance tasks compared to tasks reflecting generalization of the treatment skillset. Some participants exhibited a stronger response to treatment, whereas others showed a mixed or weaker response. Parents liked the treatment protocol, perceived benefits from intensive intervention, and indicated they would strongly recommend LSVT LOUD to other parents who have children with DS and motor speech disorders. CONCLUSIONS: These preliminary results show that children with DS tolerated intensive voice treatment without adverse effects and made select meaningful therapeutic gains. The treatment evidence from this study warrants Phase II treatment studies using LSVT LOUD with a larger group of children with DS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".