Meta-analysis of PECS with individuals with ASD: investigation of targeted versus non-targeted outcomes, participant characteristics, and implementation phase

The Picture Exchange Communication System (PECS) is a widely used picture/icon aided augmentative communication system designed for learners with autism and other developmental disorders. This meta-analysis analyzes the extant empirical literature for PECS relative to targeted (functional communication) and non-targeted concomitant outcomes (behavior, social skills, and speech) for learners with autism, learners with autism and intellectual disabilities and those with autism and multiple disabilities. Effect size analyses were done using the Improvement Rate Difference method, an advanced metric. Effect sizes were independently analyzed for targeted and non-targeted outcomes, student age, learner disability, and number of phases in the PECS protocol acquired by learners. Results supported the judgment that PECS is a promising intervention method. Analysis also revealed that functional communication outcomes associated with the PECS protocol were most impacted, that preschool children and those with autism generally showed the strongest training effects, and that in general students who advanced through the most PECS protocol phases had the best outcomes.     

Vocabulary development in children with hearing loss: the role of child, family, and educational variables

In the present study we examined the effect of hearing status on reading vocabulary development. More specifically, we examined the change of lexical competence in children with hearing loss over grade 4-7 and the predictors of this change. Therefore, we used a multi-factor longitudinal design with multiple outcomes, measuring the reading vocabulary knowledge in children with hearing loss from grades 4 and 5, and of children without hearing loss from grade 4, for 3 years with two word tasks: a lexical decision task and a use decision task. With these tasks we measured word form recognition and (in)correct usage recognition, respectively. A GLM repeated measures procedure indicated that scores and growth rates on the two tasks were affected by hearing status. Moreover, with structural equation modeling we observed that the development of lexical competence in children with hearing loss is stable over time, and a child's lexical competence can be explained best by his or her lexical competence assessed on a previous measurement occasion. If you look back, differences in lexical competence among children with hearing loss stay unfortunately the same. Educational placement, use of sign language at home, intelligence, use of hearing devices, and onset of deafness can account for the differences among children with hearing loss.     

Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study

IntroductionLiver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset.MethodsThe Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy.ResultsThe performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%–99.91%) to 100.00% (95% CI: 86.77%–100.00%), 91.30% (95% CI: 71.96%–98.93%) to 100.00% (95% CI: 83.89%–100.00%)and 94.00% (95% CI: 83.45%–98.75%) to 100.00% (95% CI: 92.45%–100.00%), respectively.ConclusionML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT.Implications for practiceDifferentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.     

基于DICOM的医学影像设备接口设计与实现

医学影像存档与通讯系统(Picture Archiving and Communication Systems,PACS)是目前医院信息化建设的热点,医学数字成像和通信标准(Digital Imaging and Communication in Medicine,DICOM)是有关医学图像及其相关信息的数据编码及通讯的国际标准,支持DICOM标准是医学影像设备并入PACS网络的必要条件。为使目前尚不符合DICOM标准的影像设备有效并入PACS系统,必须为其添加DICOM接口。我们介绍了DICOM信息模型并实现了接口的软件系统,重点介绍了应用VisualC 编程实现DICOM服务中的C-STORE和DCM文件的读写功能。     

在我国建立PACS体系要解决的问题

医学影像存档与通信系统（ＰＡＣＳ）是当前在医学影像学中具有广阔发展前景的技术领域。我国正处在建立ＰＡＣＳ的起步阶段,借鉴国外的经验,我们提出在建立ＰＡＣＳ时应注意的问题和亟待解决的问题。包括标准化问题、使用方便问题、大容量存储问题以及经济问题等。这些问题的有效解决对促进ＰＡＣＳ在我国的建立将起到推动作用．同时也向我国从事医学影像计算机图像处理和计算机放射科管理工作人员提出需要研究的课题。     

看图对话工具在妊娠期糖尿病患者健康教育中的应用效果

目的 分析看图对话工具在妊娠期糖尿病(GDM)患者健康教育中的应用效果.方法 选取2018年1月-2019年12月广西中医药大学附属瑞康医院收治的80例GDM患者,随机分为对照组和观察组,每组40例.对照组开展常规健康教育,观察组采用看图对话工具开展健康教育.比较两组健康教育前后的血糖指标和自护行为能力评分、自然分娩率...     

Learning and Processing Abstract Words and Concepts: Insights From Typical and Atypical Development

The paper describes two plausible hypotheses concerning the learning of abstract words and concepts. According to a first hypothesis, children would learn abstract words by extracting co-occurrences among words in linguistic input, using, for example, mechanisms as described by models of Distributional Semantics. According to a second hypothesis, children would exploit the fact that abstract words tend to have more emotional associations than concrete words to infer that they refer to internal/mental states. Each hypothesis makes specific predictions with regards to when and which abstract words are more likely to be learned; also they make different predictions concerning the impact of developmental disorders. We start by providing a review of work characterizing how abstract words and concepts are learned in development, especially between the ages of 6 and 12. Second, we review some work from our group that tests the two hypotheses above. This work investigates typically developing (TD) children and children with atypical development (developmental language disorders [DLD] and autism spectrum disorder [ASD] with and without language deficits). We conclude that the use of strategies based on emotional information, or on co-occurrences in language, may play a role at different developmental stages.     

实时质控加图片质控在ICU轮转护士护理质控中的效果

目的 探讨实时质控及图片质控在重症医学科（ICU）轮转护士护理质控中的应用效果。方法 将我院2016年1月1日~12月31日在ICU 轮转的71名护士设为对照组,2017年1月1日~12月31日在ICU轮转的87名护士设为观察组。对照组进行常规护理质控,观察组在常规护理质控的基础上,增加实时质控和图片质控。比较两组轮转护士护理缺陷、差错的发生率,临床护理质量评分,患者的满意度。结果 观察组病情观察、管道护理、基础护理、护理文书、核心技能、患者满意度均高于对照组,差异有统计学意义（P＜0.05）;观察组轮转护士出现药物外渗、非计划拔管、仪器操作失误、给药错误、交接班遗漏等发生情况均低于对照组,差异有统计学意义（P＜0.05）。结论 通过实时质控加图片质控能有效的提高ICU轮转护士的护理质量意识和临床护理能力,减少护理差错的发生,提高了临床护理质量和患者的满意度,有利于护理质量的持续改进。