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Combining Nonword and Sentence Repetition to Identify Developmental Language Disorder
Giang T Pham1, Ngoc Do1,2, Michelle N Ramos1
1School of Speech, Language, and Hearing Sciences, San Diego State University, San Diego, California, USA.
Combining nonword repetition and sentence repetition tasks improves the identification of developmental language disorder (DLD) in young Vietnamese children. This combined approach offers a more accurate diagnostic tool for DLD.
Area of Science:
- Linguistics
- Developmental Psychology
- Clinical Linguistics
Background:
- Nonword repetition and sentence repetition are established tools for identifying developmental language disorder (DLD).
- Previous research indicates variable diagnostic accuracy across different age groups, measures, and languages.
- The combined diagnostic utility of these repetition tasks for DLD remains understudied.
Purpose of the Study:
- To compare the diagnostic accuracy of nonword repetition and sentence repetition individually versus in combination for DLD identification.
- To evaluate the utility of combined repetition tasks in identifying DLD in young children.
Main Methods:
- 196 monolingual Vietnamese children aged 4-6 were assessed, including 168 with typical development (TD) and 28 with DLD.
- Logistic regression and ROC curves were employed to determine cutpoints and diagnostic accuracy.
- Diagnostic accuracy was calculated for each task separately and for the combined tasks.
Main Results:
- Individual tasks showed adequate sensitivity but insufficient specificity for DLD identification (NWR: 89.3% sensitivity, 41.1% specificity; SR: 82.1% sensitivity, 76.2% specificity).
- The combination of nonword and sentence repetition demonstrated adequate sensitivity (85.7%) and specificity (81.6%).
- The combined approach yielded informative likelihood ratios (positive: 4.65, negative: 0.18).
Conclusions:
- Combining nonword and sentence repetition tasks enhances the accurate identification of DLD in 4- to 6-year-old Vietnamese children.
- These tasks may capture different underlying linguistic weaknesses contributing to DLD.
- Further validation and automation of the combined model are recommended for clinical practice.
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