Search

Research on the effects of the socioeconomic status (SES) on literacy development of children with intellectual disabilities (ID) is limited. This study aimed to explore the SES's influence on literacy development of children with ID and possible mediating effects of home literacy environment (HLE) and the parent-child relationship. The subjects of this study were 390 children with ID from special needs schools in 14 regions of China, including 239 boys and 151 girls aged 7 to 15 years. This study adopted the network survey method, with parents of children with ID completing surveys on SES, HLE, literacy development, and the parent-child relationship. SES was found to positively predict literacy development of children with ID. Further, it was found that HLE played a mediating role in the prediction of literacy development by SES, and the parent-child relationship regulated the positive impact of HLE on literacy development. In conclusion, SES significantly influences literacy development of children with ID which is partially mediated by HLE. The parent-child relationship positively regulates the regression relationship between HLE and literacy development. [ABSTRACT FROM AUTHOR]

Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is widely used for the prediction of ischemic stroke outcomes. By identifying risk factors, predicting the risk of poor prognosis and thus developing personalized treatment plans, it effectively reduces the probability of poor prognosis, leading to more effective secondary prevention. This review includes 41 studies since 2018 that used ML algorithms to build prognostic prediction models for ischemic stroke, transient ischemic attack (TIA), and acute ischemic stroke (AIS). We analyzed in detail the risk factors used in these studies, the sources and processing methods of the required data, the model building and validation, and their application in different prediction time windows. The results indicate that among the included studies, the top five risk factors in terms of frequency were cardiovascular diseases, age, sex, national institutes of health stroke scale (NIHSS) score, and diabetes. Furthermore, 64% of the studies used single-center data, 65% of studies using imbalanced data did not perform data balancing, 88% of the studies did not utilize external validation datasets for model validation, and 72% of the studies did not provide explanations for their models. Addressing these issues is crucial for enhancing the credibility and effectiveness of the research, consequently improving the development and implementation of secondary prevention measures. [ABSTRACT FROM AUTHOR]