Purpose: Many children with communication disorders (CDs) experience lengthy gaps between parental reporting of concerns and formal identification by professionals. This means that children with CDs are denied access to early interventions that may help support the development of communication skills and prevent possible negative sequelae associated with long-term outcomes. This may be due, in part, to the lack of assessment instruments available for children younger than 3 years of age. This study therefore reports on promising preliminary data from a novel set of valid dynamic assessment (DA) measures designed for infants.
Method: We recruited 53 low-risk children and two groups of children considered to be at high risk for CDs ( = 17, social high risk, and = 22, language high risk) due to family members with language and social communication difficulties. The children were between 1 and 2 years of age and were assessed using a battery of five DA tasks related to receptive vocabulary, motor imitation, response to joint attention, turn taking, and social requesting. A set of standardized measures were also used.
Results: The DA tasks showed high levels of interrater reliability and relationships with age across a cross-sectional sample of children from the low-risk group. Three tasks showed moderate to strong correlations with standardized measures taken at the same age, with particularly strong correlations between the DA of receptive vocabulary and other receptive language measures. The DA of receptive vocabulary was also the only task to discriminate between the three risk groups, with the social high risk group scoring lower.
Conclusions: These results provide preliminary information about early DA tasks, forming the basis for further research into their utility. DA tasks might eventually facilitate the development of new methods for detecting CDs in very young children, allowing earlier intervention and support.
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http://dx.doi.org/10.1044/2022_AJSLP-22-00040 | DOI Listing |
Sci Rep
December 2024
School of Physical Education, Southwest Petroleum University, Chengdu, 610500, China.
Stroke is one of the leading causes of death in developing countries, and China bears the largest global burden of stroke. This study aims to investigate the relationship between different dimensions of physical activity levels and stroke risk using a nationally representative database. We performed a cross-sectional analysis using data from the China Health and Retirement Longitudinal Study (CHARLS) 2020.
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December 2024
The School of Nursing, Fujian Medical University, No. 1 Xuefu North Road, Fuzhou, 350122, Fujian, China.
Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e.
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December 2024
Weather Program Office, Ocean and Atmospheric Research, NOAA, Silver Spring, MD, USA.
Tropical cyclone risks are expected to increase with climate change. One such risk is extreme ocean waves generated by surface winds from these systems. We use synthetic databases of both historical (1980-2017) and future (2015-2050) tropical cyclone tracks to generate wind fields and force a computationally efficient wave model to estimate significant wave heights across all global tropical cyclone basins.
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December 2024
School of Pharmacy, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Cuproptosis, a newly identified form of cell death, has drawn increasing attention for its association with various cancers, though its specific role in colorectal cancer (CRC) remains unclear. In this study, transcriptomic and clinical data from CRC patients available in the TCGA database were analyzed to investigate the impact of cuproptosis. Differentially expressed genes linked to cuproptosis were identified using Weighted Gene Co-Expression Network Analysis (WGCNA).
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December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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