Intensive Voice Treatment (LSVT LOUD) for Children With Spastic Cerebral Palsy and Dysarthria
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the effects of an intensive voice treatment (Lee Silverman Voice Treatment, commonly known as LSVT LOUD) for children with spastic cerebral palsy (CP) and dysarthria. METHOD: A nonconcurrent multiple baseline single-subject design with replication across 5 children with spastic CP was used. Auditory-perceptual analysis of speech, acoustic measures of vocal functioning, and perceptual ratings by parents of participants were obtained at baseline, posttreatment, and 6-week follow-up recording sessions. RESULTS: Listeners consistently preferred the speech samples taken immediately posttreatment over those taken during the baseline phase for most perceptual characteristics rated in this study. Changes in acoustic measures of vocal functioning were not consistent across participants and occurred more frequently for maximum performance tasks as opposed to speech. Although parents of the treated participants reported an improved perception of vocal loudness immediately following treatment, maintenance of changes at 6-week follow-up varied across the participants. No changes were observed in the 5th participant, who did not receive treatment. CONCLUSIONS: These findings provide some preliminary observations that the children with spastic CP in this study not only tolerated intensive voice treatment but also showed improvement on select aspects of vocal functioning. These outcomes warrant further research through Phase 2 treatment studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".