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Record W4214810081 · doi:10.1044/2021_jslhr-21-00228

Intensive Voice Treatment (Lee Silverman Voice Treatment [LSVT LOUD]) for Children With Down Syndrome: Phase I Outcomes

2022· article· en· W4214810081 on OpenAlexaff

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2022
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsPhase (matter)Voice therapyAdverse effectPhonationBiofeedbackLanguage disorder

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.404
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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