Objective: The purpose of this study was to focus on two linguistic abilities, word retrieval (expressive language) and comprehension of vocabulary and grammar (receptive language), as well as to investigate to what extent long-term problems exist in these areas in children following traumatic brain injury.
Methods: Two groups of children were studied retrospectively: twenty-four children with traumatic brain injury (TBI) and twenty-one children diagnosed with brain tumour. All children had been referred to the rehabilitation team for assessment. The children were between four and seventeen years old when assessed, with the assessments performed at least one year after the injury or brain tumour diagnosis. An established set of tests regarding word retrieval and comprehension of vocabulary and grammar was used, and the results were compared with normative test data.
Results: In both clinical groups, significantly more children scored lower than the designated "normal" score than in the normative sample on tests measuring confrontation naming and phonological word retrieval. In addition, in the brain tumour group, more children demonstrated significantly lower results than normal performance on a test for semantic word retrieval. In the TBI group, significantly more children scored below the normal value on tests of word and grammatical comprehension when compared to the normative sample.
Conclusions: This study confirms that word retrieval is an area of deficit in many children with acquired brain injuries one year or more after the injury occurred. The study also indicates that children with TBI may have persistent deficits in comprehension of both vocabulary and grammar.
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http://dx.doi.org/10.3233/PRM-2010-0137 | DOI Listing |
Sci Rep
January 2025
School of Economics and Management, Beijing jiaotong University, Shandong, 264401, China.
Text Graph Representation Learning through Graph Neural Networks (TG-GNN) is a powerful approach in natural language processing and information retrieval. However, it faces challenges in computational complexity and interpretability. In this work, we propose CoGraphNet, a novel graph-based model for text classification, addressing key issues.
View Article and Find Full Text PDFFront Behav Neurosci
December 2024
Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastián, Spain.
Psychol Res
December 2024
Dpto Metodología and ERI-Lectura, Universitat de València, Av. Blasco Ibáñez, 21; 46010, Valencia, Spain.
Brand names typically maintain a distinctive letter case (e.g., IKEA, Google).
View Article and Find Full Text PDFSci Rep
December 2024
Department of CSE, Adama Science and Technology University, Oromia, Ethiopia.
Afaan Oromo is a resource-scarce language with limited tools developed for its processing, posing significant challenges for natural language tasks. The tools designed for English do not work efficiently for Afaan Oromo due to the linguistic differences and lack of well-structured resources. To address this challenge, this work proposes a topic modeling framework for unstructured health-related documents in Afaan Oromo using latent dirichlet allocation (LDA) algorithms.
View Article and Find Full Text PDFEur J Phys Rehabil Med
December 2024
Laboratory of Neuropsychology, Department of Neurorehabilitation Sciences, IRCCS Istituto Auxologico Italiano, Milan, Italy.
Background: The defective spoken output of persons with aphasia has anomia as a main clinical manifestation. Improving anomia is therefore a main goal of any language treatment.
Aim: This study assessed the effectiveness of a novel, 2-week, rehabilitation protocol (PHOLEXSEM), focused on PHonological, SEmantic, and LExical deficits, aiming at improving lexical retrieval, and, generally, spoken output.
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