ObjectiveThe purpose of this study was to investigate the therapeutic effects of transcranial direct current stimulation on swallowing function in poststroke patients.DesignWe searched for potentially eligible randomized controlled trials from electronic databases, including the PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang, and Chinese Science and Technology Periodical (VIP) databases, from their inception to January 15, 2021. All statistical analyses were performed using RevMan 5.4, and the standardized mean difference with 95% confidence intervals was estimated for the swallowing function outcomes and to understand the mean effect size.ResultsTen studies involving 343 participants were included in this meta-analysis. The overall analyses demonstrated a significant effect size for swallowing function. Subgroup analyses suggested that both acute and chronic stroke patients showed significant effects on swallowing function after transcranial direct current stimulation. Furthermore, compared with sham stimulation, transcranial direct current stimulation anodal to the affected, unaffected, and bilateral hemispheres can produce a significant effect size for swallowing function in stroke patients.ConclusionsThis meta-analysis showed that transcranial direct current stimulation is likely to be effective for the recovery of dysphagia in poststroke patients, in the acute or chronic phase, and that the effect of anodal transcranial direct current stimulation to unaffected hemispheres is larger.

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This article presents a novel method, the Complex Dynamics Essay Scorer (CDES), for automated essay scoring using complex network features. Texts produced by college students in China were represented as scale-free networks (e.g., a word adjacency model) from which typical network features, such as the in-/out-degrees, clustering coefficient (CC), and dynamic networks, were obtained. The CDES integrates the classical concepts of network feature representation and essay score series variation. Several experiments indicated that the network measures different essay qualities and can be clearly demonstrated to develop complex networks for autoscoring tasks. The average agreement of the CDES and human rater scores was 86.5%, and the average Pearson correlation was .77. The results indicate that the CDES produced functional complex systems and autoscored Chinese essays in a method consistent with human raters. Our research suggests potential applications in other areas of educational assessment. 478

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