"五习惯"医患沟通评价量表的构建及信效度研究

背景 医患沟通障碍是导致医疗纠纷发生的主要原因之一。目前，我国对医务人员医患沟通能力的评价研究较少，缺乏信效度良好的评估工具。 目的 构建"五习惯"医患沟通评价量表（5HCS），检验其信度和效度。 方法 2014年3月，采用Brislin翻译法在"四习惯"医患沟通评价体系（4HCS）的基础上，形成5HCS初稿；2014年4—6月，采用德尔菲法，使用问卷分两轮征求专家意见，根据专家意见修订条目后形成5HCS定稿；2018年3月，使用新构建的5HCS对127名住院医师的医患沟通能力进行评估，通过分析其评价数据，检验量表的内部一致性、评价者间信度、内容效度和标准关联效度。 结果 正式版5HCS包含5个维度（"尊重示善，融洽关系""采集信息，引导观点""表达共情，建立信任""风险告知，知情同意""提供诊断，协商决策"），21个条目。量表的Cronbach's α系数为0.716，各维度与量表总分的r值为0.524~0.692，各条目的内容效度指数（I-CVI）≥0.81，量表总分的评价者间信度r值为0.912，组内相关系数（ICC）=0.912，标准关联效度以中文版医患沟通技能评价量表（SEGUE）为标准，两个量表总分之间的r值为0.377（P<0.01）。 结论 5HCS具有良好的信度和效度，可将其作为我国住院医师医患沟通能力测评工具加以推广应用。     

运动学对线应用于全膝关节置换术的研究进展

全膝关节置换术是目前治疗终末期膝关节骨性关节炎最有效的方法之一，全膝关节置换术后膝关节功能、术后膝关节疼痛与术后下肢力线有着密切的关系。目前膝关节置换术下肢对线方式主要采用机械力学对线方法，该方法可以改善膝关节功能，但是有部分患者也会产生术后膝关节的疼痛、活动受限等并发症，导致术后患者满意度较差。近些年越来越多的研究开始关注运动力学对线技术并应用于临床。运动学对线技术旨在重建患者的解剖结构，将膝关节假体运动轴与膝关节生理性运动轴保持一致，减少膝关节周围软组织和韧带的松解，实现膝关节生理性运动。该文就运动力学对线方法在全膝关节置换术中的应用进展作一综述。     

心脏瓣膜置换术后患者的出院准备度现状及其影响因素分析

目的:调查心脏瓣膜置换术后患者的出院准备度现状,并分析其影响因素。方法:于2018年9月至2019年3月用一般资料、出院准备度量表、出院指导质量量表对139例瓣膜置换术后患者进行调查。结果:心脏瓣膜置换术后患者的出院准备度总分为(89.51±8.53)分,与出院指导质量呈正相关;多元回归分析显示,主要照顾者、婚姻状况、工作状态、服药种类、出院指导质量为出院准备度影响因素。结论:心脏瓣膜置换术后患者的出院准备度有待提高,患者感知的个人状态得分不理想;护士应鼓励患者配合进行早期康复,强化出院指导,采取有效措施提供康复信息,满足患者及其家属需求,提高出院准备度。     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

向楠治疗亚急性甲状腺炎经验

向楠教授系湖北中医药大学二级教授、主任医师、博士生导师,湖北中医名师、首届湖北省中青年知名中医、武汉中青年中医名医,培养学术继承人及硕士、博士等50余名,从事中医临床、教学、科研工作30余载,擅长应用中医药防治内分泌及代谢性疾病。余有幸跟师学习,受益颇深,现将其治疗亚急性甲状腺炎的经验总结于下,与同道共享。     

低密度脂蛋白在2型糖尿病骨质疏松中的作用研究进展

2型糖尿病及骨质疏松已成为我国最主要的慢性代谢性疾病。2型糖尿病常伴有血脂紊乱，常表现为低密度脂蛋白升高，而越来越多的研究表明血脂通过不同的方式影响骨代谢，其机制可能是通过抑制骨髓间充质干细胞成骨分化，通过RANK/RNAKL/OPG信号通路及炎症反应调节破骨细胞等方式调节骨代谢。     

HBV患者血清YKL-40、CA19-9、GP73水平与其病情相关性

目的 分析HBV患者YKL-40、CA19-9、GP73水平差异及与患者病情轻重程度的相关性,探讨HBV患者病情的判定指标。方法 选取2015年5月—2018年5月收治的100例HBV患者,其中慢性HBV感染组40例、慢性乙型肝炎组36例、HBV相关肝硬化组24例,同期选择我院健康体检的健康者50例作为健康对照组;检测患者血中YKL-40、CA19-9、GP73水平;分析HBV感染患者血清YKL-40、CA19-9、GP73水平与病情轻重程度的相关性。结果 慢性HBV感染、慢性乙型肝炎及HBV相关肝硬化患者血中YKL-40水平分别为（36.38±4.19）ng/mL 、（49.02±4.32）ng/mL、（65.14±5.21）ng/mL ,CA19-9分别为（12.03±1.03）KU/L、（13.84±0.98）KU/L、（16.94±0.81）KU/L,GP73分别为（47.22±5.38）ng/mL 、（98.53±10.24）ng/mL 、（229.85±12.19）ng/mL,均明显高于对照组的（28.19±3.27）ng/mL 、（7.34±0.92）KU/L 、（30.93±3.89）ng/mL,均P=0.000 0。随着慢性HBV感染者、慢性乙型肝炎患者和不同HBV相关肝硬化患者肝脏炎症及纤维化程度加重,患者血中YKL-40、CA19-9和GP73也随之显著增加,均P=0.000 0;YKL-40、CA19-9和GP73均是影响HBV感染患者体内炎症坏死及肝脏纤维化的独立性影响因素,差异有统计学意义（P<0.05）。结论 HBV感染患者血清中YKL-40、CA19-9、GP73水平是HBV感染患者病情轻重程度的独立性影响因素。