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Record W4301391839 · doi:10.2196/40465

The Use of Segmental and Suprasegmental Sequencing Skills to Differentiate Children With and Without Childhood Apraxia of Speech: Protocol for a Comparative Accuracy Study

2022· article· en· W4301391839 on OpenAlexvenueno aff
Min Ney Wong, Eddy C. H. Wong, Shelley L. Velleman

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySyllableVietnameseAudiologyVariation (astronomy)Repetition (rhetorical device)Task (project management)Speech recognitionCognitive psychologyLinguisticsComputer scienceMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Childhood apraxia of speech (CAS) is a motor-based speech sound disorder (SSD) with a core impairment in the planning and programming of spatiotemporal parameters of speech movement sequences. CAS may cause deficits in both segmental and suprasegmental components of speech, and it can severely affect children's ability to speak intelligibly and communicate effectively and impact their quality of life. Assessment tasks, such as the maximum performance tasks (MPT) and Syllable Repetition Task (SRT), examine children's segmental sequencing skills to assist with the diagnosis of CAS. In Hong Kong, although the MPT and SRT have been used clinically to diagnose CAS in Cantonese-speaking children, their validity has not been reported. There is an urgent need for such investigations. Suprasegmentally, lexical stress errors have been reported as a consensual feature and to aid in the diagnosis of CAS. However, there are challenges in diagnosing CAS in children who speak tonal languages like Cantonese. A recent study has reported lexical tone errors in Cantonese-speaking children with CAS. Furthermore, deficits in pitch-variation skills were found in Cantonese-speaking children with CAS using a tone sequencing task (TST). It is hypothesized that there is a universal deficit in pitch-variation skills among tonal and nontonal language speakers with CAS. Further investigations of pitch-variation skills using the TST in Cantonese-speaking children with CAS may shed light on suprasegmental deficits in tonal languages and contribute to the development of a valid diagnostic tool for CAS in children who speak other tonal languages, such as Vietnamese, Thai, and Mandarin. OBJECTIVE: This study aims to examine the diagnostic potential of the MPT, SRT, and TST in diagnosing Cantonese-speaking children with CAS and to investigate pitch-variation skills in Cantonese-speaking children with and without CAS. METHODS: A total of 25 children with CAS and 3 groups of age- and gender-matched controls (non-CAS SSD only group, non-CAS SSD co-occurring with language impairment group, and typical development group) will be recruited. All participants will perform the MPT, SRT, and TST measures. Their performances on these tools will be perceptually judged and acoustically measured. RESULTS: Data collection will last from January 1, 2022, to October 30, 2023. As of August 2022, the project has recruited 4 children in the CAS group, 21 children in the non-CAS SSD group, 4 children in the speech and language impairment group, and 53 children in the typical development group. CONCLUSIONS: It is anticipated that Cantonese-speaking children with CAS will have poorer pitch-variation skills than the control groups and that the MPT, SRT, and TST will be appropriate diagnostic tools for identifying CAS in Cantonese-speaking children. The project will benefit the field of speech-language pathology locally and internationally, with short- and long-term impacts. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40465.

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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.213
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.276
GPT teacher head0.533
Teacher spread0.257 · 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
GenreProtocol

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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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