"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。     

hsacirc0006867在结直肠癌组织中的表达及其临床意义北大核心

目的:探讨环状RNA hsa_circ_0006867在结直肠癌中的表达及其与临床病理因素的关系。方法:全转录组测序筛选结直肠癌中特异circRNAs表达谱,挑选出差异表达显著的hsa_circ_0006867,qRT-PCR检测54例结直肠癌组织及癌旁组织中hsa_circ_0006867表达情况,分析其表达水平与结直肠癌临床病理特征的相关性,ROC曲线分析hsa_circ_0006867在结直肠癌中的诊断价值。结果:测序获得circRNAs在结直肠癌中的差异表达谱,qRT-PCR验证hsa_circ_0006867在结直肠癌中表达下调(P<0.05)。其表达水平与肿瘤分化程度和远处转移有关(P<0.05)。ROC曲线显示hsa_circ_0006867诊断结直肠癌AUC为0.851(95%CI:0.775~0.927),当截断值为0.0146时,敏感度为88.46%(95%CI:0.770~0.946),特异度为73.08%(95%CI:0.598~0.832),差异具有统计学意义(P<0.001)。结论:hsa_circ_0006867在结直肠癌中表达下调,与相关临床病理特征密切联系,可作为潜在结直肠癌临床诊断指标。     

华坪县2013-2015年健康人群麻疹抗体水平监测结果分析

目的了解华坪县健康人群麻疹抗体水平,发现工作薄弱地区和免疫空白人群,为实现消除麻疹的目标提供科学依据。方法采用分层抽样方法对华坪县8个乡镇不同年龄段健康人群开展麻疹抗体监测,采用间接酶联免疫吸附试验(ELISA间接法)检测血清麻疹Ig G抗体。结果 2013-2015年共检测1 106人,麻疹抗体总阳性率96.47%。其中,阳性率较低的是2014年(93.13%),人群是8~17月龄组(89.22%)。尚有新庄乡(89.92%)、船房乡(93.98%),通达乡(94.86%)3个乡镇麻疹抗体阳性低于95%。有免疫史者人群阳性率较无免疫史者高(χ~2=5.1830,P=0.023),男女性别间无差异。结论华坪县人群麻疹免疫效果较好,但存在免疫薄弱地区和免疫空白人群,应结合该监测结果,加强重点乡镇麻疹疫苗两剂次接种工作和抗体阴性人群的重新免疫接种。     

糖尿病黄斑水肿患者病变程度与脉络膜厚度的关系

目的　利用频域光学相干断层扫描深度增强（enhanced depth imaging spectral domain optical coherence tomography，EDI SD-OCT）观察糖尿病黄斑水肿（diabetic macular edema，DME）患者脉络膜厚度（choroidal thickness，CT）的变化及结构特点，探讨DME病变程度与CT的关系。方法　纳入2型糖尿病患者共123例204眼，其中69眼诊断为DME（DME组），135眼无黄斑水肿为对照组。DME眼依据OCT形态学特点进一步分为视网膜弥漫性增厚（diffuse retinal thickness，DRT）型（34眼）、黄斑囊样水肿（cystoid macular edema，CME）型（19眼）和浆液性视网膜脱离（serous retinal detachment，SRD）型（16眼），利用EDI-OCT分别测量黄斑中心凹下CT和以黄斑为中心上、下、鼻、颞500 μm、1000 μm、1500 μm、2000 μm处CT。结果　DME组黄斑中心凹下CT为（326.72±90.15）μm，对照组为（320.17±106.46）μm，两组之间无统计学差异，但黄斑中心凹下CT与视网膜厚度间具有明显正相关关系（r=0.270，P=0.025）。DME亚型CT分别为:DRT型（303.94±81.47）μm、CME型（304.42±73.98）μm和SRD型（401.63±88.80）μm，SRD型CT明显高于其他亚型（P<0.05），此外，SRD型的周边CT同样呈现均匀一致的增厚；鼻侧CT从500 μm至2000 μm呈距离敏感性降低（P<0.05），但SRD型鼻侧CT降低幅度明显变缓（P=0.195）。结论　SRD型黄斑水肿患者CT在中心凹下及周边部均显著增厚，CT与DME病变程度之间有一定相关性。     

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.     

柯氏模型在重症医学科护理规培生身体约束培训中的应用

目的 探讨应用柯氏评估模型评价重症医学科护理规培生对身体约束培训的效果.方法 选取符合纳入和排除标准的57例重症医学科护理规培生,采用前后对照,2018年1月—6月的重症医学科护理规培生为对照组,28例;2018年7月—12月的重症医学科护理规培生为观察组,29例.对照组采用身体约束常规临床经验培训教育和管理,观察组采用规范化培训身体约束和管理.采用柯氏评估模型,对从反应层面、学习层面、行为层面3个方面进行效果评估,评估时间为干预前、干预第1个月、干预第2个月.结果 观察组对培训满意度、对身体约束知识掌握、对身体约束实施情况,与对照组比较,差异均具有统计学意义(P<0.05).结论 对重症医学科护理规培生采用规范化培训身体约束及管理,不仅能提高护理规培生对培训的满意度,还能随着时间的推移,加固对身体约束培训知识的掌握,改善对患者身体约束的情况.