网络互动平台在人体解剖学教学中的应用与研究

随着互联网的普及利用和计算机网络技术的发展,各种网络交互技术能够为师生之间实现教学信息及时充分交流提供了极大的便利,本文通过对基于人体解剖学网络互动平台教学组织与实施进行分析探讨网络互动平台在高校人体解剖学中的应用并为利用网络互动平台进行教学的老师提供参考和借鉴.     

学习自主性、学习倦怠与自测健康的关系模型

目的:了解青年学生的学习倦怠、自测健康和学习自主性状况,进而探索这三个变量之间的相互关系模型.方法:选取德州市青年学生共1925人进行问卷调查,对所得数据进行结构方程模型探讨.结果:青年学生在学习倦怠、自测健康、学习自主性各维度间都存在显著相关(相关系数绝对值在0.15到0.70之间,P＜0.01).用不同群体验证学习自主性-学习倦怠-自测健康路径模型拟合良好(x2/df＜3.19,GFI、CFI、TLI、NFI＞0.91,RMSEA＜0.064).模型表明,学习自主性越高,学习倦怠水平越低(路径系数为-0.90,P＜0.001);同时学习倦怠水平越低,自测健康水平越高(路径系数为-0.54,P＜0.001).结论:学生的学习自主性影响学习倦怠水平,进而影响自测健康状况.     

生理学互动型学习网站的实践与探索

网络课程是基于现代多媒体、网络教学环境的教学资源。为提高教学质量,设计和开发了生理学互动型学习网站。内容包括：网络课堂、学习园地、在线测试和网络资源库等。教学实践显示,利用网站组织教学,具有课程内容丰富、重点突出、图文并茂,并具有较好的交互性等优点。     

神经病学教与学网络互动平台模式的探索

神经病学是一门综合性比较强的学科,与内科、外科、解剖学、病理生理等学科密切相关,因为神经解剖结构的复杂性,以及神经内科的实践性和专业性较强而课时相对较少,成为临床教与学的难点之一,通过建立神经内科网络互动平台,促进神经内科教学水平的提高,或许是一种有益的教改探索。     

神经病学教与学网络互动平台模式的探索

神经病学是一门综合性比较强的学科,与内科、外科、解剖学、病理生理等学科密切相关,因为神经解剖结构的复杂性,以及神经内科的实践性和专业性较强而课时相对较少,成为临床教与学的难点之一,通过建立神经内科网络互动平台,促进神经内科教学水平的提高,或许是一种有益的教改探索.     

“基于网络互动平台的《医学寄生虫学》PBL教学”初步实践

为适应新的＂生物-心理-社会＂医学模式对医学寄生虫学教学的要求,开展基于问题的学习（PBL）模式教学成为现今教学改革中的常用手段,但是经典的PBL模式教学在实践中又面临诸多困难,如教学时间、师资配备和教学资源有限等。网络教学具有资源丰富、互动快捷、灵活高效等优点,为教学提供了极佳的技术平台。本研究结合两者的优势,开展了＂基于网络互动平台的《医学寄生虫学》PBL教学＂实践,对教学过程中遇到的各种问题和解决方法进行了总结,并提出改进计划。     

围绕学生的不同学习阶段 打造针对性的人体解剖学教学平台

人体解剖学教学在医学教育中占据重要地位,除了通常的狠抓课堂教学和实验教学外,还根据学生不同的学习阶段打造有针对性的人体解剖学教学平台,营造良好的学习氛围。     

浅谈手术学教学中的互动作用

手术学是临床外科学的基础,是一门以技能为主,衔接理论与实际操作的桥梁课。在手术学教学中,更新观念,使医学技能训练和全面发展学生能力在理论和实践的更高层次上紧密结合,是技能教学深入改革和发展的方向和趋势。为此,如何运用教学过程最优化原理,充分发挥教学过程中教师与学生的互动效应,是实现临床医学专业培养目标的必要条件之一。1教师的主导作用1.1树立教师的典范作用手术学课程的基本内容包括无菌技术和外科手术基本操作技术,如何在手术学的教学过程中建立并强化医学生的无菌观念,使之掌握外科手术的基本操作技能,是每一位教师都应…     

基于"云解剖"教学平台构建医学生智慧学习路网

为加快教育现代化和教育强国建设,国希望"云解剖"教学平台,为师生提供了有效的支撑资源和工具,实现教师在平台上自创空间,精选教学资源备课,把人体解剖学知识点汇集成知识族,开展线上教学、线上布置作业、线上教学效果检测、线上考试等,针对所教学生进行个性化教学.学生通过手机端"云解剖"教学平台,在线听课、做实验、刷题、相互交流学习经验,构建智慧学习路网,"云解剖"教学平台不下课的解剖学老师.     

互动教学-一种值得提倡的医学教育模式

在当今知识信息爆炸的时代，医学得到迅猛发展，医疗形势发生了根本的变化。新形势下，传统的医学教育模式面临着新的挑战，单、被动的学习方式往往使医学生们感到枯燥、乏味。因此必须加大医学教学模式改革的力度，我们认为，多样结合、双向互动的教学模式应该成为医学教育的主要形式。本文就新形势下如何以灵活的互动教学形式开展好医学教育工作做一下初步探讨。     

北京协和医院进修医师院前培训在线学习模式探索与学习效果评估

目的北京协和医院对进修医师院前培训进行在线学习模式改革,并对改革效果进行评估。方法将2016年春季289名进修医师院前培训模式由传统的课堂集中授课方式改革为在线学习模式,建立在线学习体系,并通过问卷调查和复试方式对在线学习的学习后效果进行分析和评估。结果 289名进修医师完成了结构化院前培训在线课程的在线学习,初试平均及格率为42.2%。并对289人进行了调查问卷发放和复试考核,问卷收回262份,回收率90.7%,复试平均及格率为74.9%,提升了32.7%。结论在线学习方式是有效的知识传播工具,具有较好的学习效果。     

直接前入路全髋关节置换的优势及学习曲线

文题释义：直接前入路全髋关节置换：利用阔筋膜张肌与缝匠肌及股直肌与臀中肌之间的肌肉间隙显露髋关节作为全髋关节置换手术入路的一种微创技术。学习曲线：以横轴表示反复次数(探索次数)、以纵轴表示各种学习测试、学习过程的曲线。背景：近年来,随着现代外科手术向着微创和“快速康复”的趋势发展,直接前入路开始受到骨科医师的青睐。目的：从直接前入路发展历史、手术适应证及患者选择、手术方法、常见并发症、术式优点、学习曲线等作一综述,以期为髋关节置换的快速康复提供思路。方法：检索PubMed、万方数据库、中国知网(CNKI)数据库1956至2020年收录的有关直接前入路全髋关节置换的文献,英文检索词为“minimally invasive,direct anterior approach,total hip arthroplasty,enhanced recovery after surgery,complication,learning curve”,中文检索词为“微创,直接前方入路,全髋关节置换术,快速康复,并发症,学习曲线”,最终纳入67篇文献进行归纳总结。结果与结论：①髋关节直接前入路因其走行于肌筋膜间隙,而且是一个真正意义上的神经肌肉界面入路,理论上不伤及重要的肌肉与神经血管,是一个十分理想的微创手术入路;②虽然直接前入路具有康复快、患者术后痛苦小、住院时间短、患者满意度高的优点,但由于国内大部分术者都是从其他入路改变过来的,存在着学习曲线长、早期并发症高等缺点。ORCID: 0000-0002-4061-2330(刘天盛)中国组织工程研究杂志出版内容重点：人工关节;骨植入物;脊柱;骨折;内固定;数字化骨科;组织工程     

Medical students' approaches to learning anatomy: Students' experiences and relations to the learning environment

To examine students' perceptions and experiences of learning anatomy, an on‐line Likert‐style questionnaire was administered during 2006 to students reading a Bachelor of Medicine 4‐ and 5‐year program (n = 256, 23.8%). Statistical analysis found that students predominantly felt that understanding anatomy and working with human cadaveric material were very important in becoming a doctor. Students reported that working on cadaveric specimens was an effective way of learning anatomy, and also found the amount of anatomy they needed to learn to be daunting. Student responses were correlated with their approaches to learning [Approaches to Studying Inventory for Students (ASSIST)] scores using a Kruskal Wallis test. Significant relationships between the approach to learning anatomy adopted and students' perception and experience of anatomy were found. A deep approach to learning anatomy correlated with students who reported that the most effective way of learning anatomy in the dissecting room was to get their hands in and feel for structures, who used anatomical terms and language at clinical opportunities, and who frequently used their anatomy‐radiology knowledge in clinical situations. A surface approach to learning anatomy was associated with elements, such as students finding anatomy learning daunting and not seeing the point to it. The outcomes provide clear associations between students' perception of the anatomy learning environment, the approach to learning adopted, and the anatomy activities in which students engage. The findings highlight that understanding and applying anatomical knowledge are best accomplished by the adoption of a deep approach to learning. Clin. Anat. 23:101–103, 2010. © 2009 Wiley‐Liss, Inc.     

Using machine learning to support healthcare professionals in making preauthorisation decisions

BackgroundPreauthorisation is a control mechanism that is used by Health Insurance Providers (HIPs) to minimise wastage of resources through the denial of the procedures that were unduly requested. However, an efficient preauthorisation process requires the round-the-clock presence of a preauthorisation reviewer, which increases the operating expenses of the HIP. In this context, the aim of this study was to learn the preauthorisation process using the dental set from an existing database of a non-profit HIP.MethodsPre-processing data techniques as filtering algorithms, random under-sample and imputation were used to mitigate problems that arise from the selection of relevant attributes, class balancing and filling unknown data. The performance of classifiers Random Tree, Naive bayes, Support Vector Machine and Nearest Neighbor was evaluated according to kappa index and the best classifiers were combined by using ensembles.ResultsThe number of attributes were reduced from 164 to 15 and also were created 12 new attributes from existing discrete data associated with the beneficiary's history. The final result was the development of a decision support mechanism that yielded hit rates above 96%.ConclusionsIt is possible to create a tool based on computational intelligence techniques to evaluate the requests of test/procedure with a high accuracy. This tool can be used to support the activities of the professionals and automatically evaluate less complex cases, like requests not involving risk to the life of patients.     

以问题为基础的学习在病理生理学教学中的应用

目的： 与传统的教学方法进行比较，探讨在本科生病理生理学教学中开展以问题为基础的学习（PBL）的必要性，并进一步探索在我国开展PBL教学的可能方法和途径。 方法： 在大学三年级本科生中抽取1个大班作为试验班，其余3个大班作为对照班，在试验班中采用全程PBL教学，将病理生理学教材内容重新整合并划分为6个单元，分别由3位指导教师承担不同单元的教学。每个单元相互独立，但又相互联系、交叉，均以1-2个病历为主要内容，每个病历中均隐藏着所涉及的学习要点的全部内容，学生通过小班讨论、自学、小组讨论、大班讨论以及教师总结等几个环节，归纳、总结出该单元的学习要点，以备进一步学习。在课程结束之后，在学生中进行问卷调查，同时采用PBL教学试验班和对照班期末考试考同一份卷的方法比较PBL教学和传统教学方式的主要差别。 结果： 采用PBL教学方式能够更好地调动学生的学习积极性，启发学生自主学习，并且在提高学生分析问题和解决问题的能力方面相对于传统的教学方式具有明显的优势。     

LEAP: Using machine learning to support variant classification in a clinical setting

Advances in genome sequencing have led to a tremendous increase in the discovery of novel missense variants, but evidence for determining clinical significance can be limited or conflicting. Here, we present Learning from Evidence to Assess Pathogenicity (LEAP), a machine learning model that utilizes a variety of feature categories to classify variants, and achieves high performance in multiple genes and different health conditions. Feature categories include functional predictions, splice predictions, population frequencies, conservation scores, protein domain data, and clinical observation data such as personal and family history and covariant information. L2‐regularized logistic regression and random forest classification models were trained on missense variants detected and classified during the course of routine clinical testing at Color Genomics (14,226 variants from 24 cancer‐related genes and 5,398 variants from 30 cardiovascular‐related genes). Using 10‐fold cross‐validated predictions, the logistic regression model achieved an area under the receiver operating characteristic curve (AUROC) of 97.8% (cancer) and 98.8% (cardiovascular), while the random forest model achieved 98.3% (cancer) and 98.6% (cardiovascular). We demonstrate generalizability to different genes by validating predictions on genes withheld from training (96.8% AUROC). High accuracy and broad applicability make LEAP effective in the clinical setting as a high‐throughput quality control layer.     

A simulation tool to support teaching and learning the operation of X-ray imaging systems

We present a software tool for the simulation of an X-ray imaging systems. It consists of three virtual objects: the X-ray source, the human body and the detector. The X-ray source is modeled as a radiological tube for which the user can modify the tube potential, the anode material, the tube load, the filtration and some geometric parameters, such as source-skin distance, orientation and field size. The virtual body consists of a 3D voxel matrix in which CT numbers for each point of the body are stored, obtained from tomographic slices. The interactions of X-rays passing through the body are evaluated using pencil beam technique. The image is obtained computing the dose absorbed by the detector and converting it into optical density by the use of a proper response function. The dose absorbed in each point of the body is also computed and can be visualized both in 2D and 3D representations. The influence of each parameter on the beam spectrum, on the image quality and on the dose to the patient can be observed interactively.     

Topic detection using paragraph vectors to support active learning in systematic reviews

Systematic reviews require expert reviewers to manually screen thousands of citations in order to identify all relevant articles to the review. Active learning text classification is a supervised machine learning approach that has been shown to significantly reduce the manual annotation workload by semi-automating the citation screening process of systematic reviews. In this paper, we present a new topic detection method that induces an informative representation of studies, to improve the performance of the underlying active learner. Our proposed topic detection method uses a neural network-based vector space model to capture semantic similarities between documents. We firstly represent documents within the vector space, and cluster the documents into a predefined number of clusters. The centroids of the clusters are treated as latent topics. We then represent each document as a mixture of latent topics. For evaluation purposes, we employ the active learning strategy using both our novel topic detection method and a baseline topic model (i.e., Latent Dirichlet Allocation). Results obtained demonstrate that our method is able to achieve a high sensitivity of eligible studies and a significantly reduced manual annotation cost when compared to the baseline method. This observation is consistent across two clinical and three public health reviews. The tool introduced in this work is available from https://nactem.ac.uk/pvtopic/.     

3D打印眼模型在医学生直接检眼镜教学中的应用

目的设计一种可模拟眼底的人眼模型,用于直接镜的眼底检查教学,并评估其有效性。方法首先应用3D打印材料制作眼部模型,然后选取进入眼科学习的北京协和医学院临床医学八年制本科生92名,平均随机分配到两组:A组(模型训练组)及B组(传统训练组)。两组学生经过相同时间的训练后,选取难度相同的真实患者眼底进行测试,以120 s为考核时限,记录是否能看清视盘、看清视盘杯盘比的反应时间及正确与否,最后汇总数据,进行统计学分析。结果 A组共43人成功看到眼底(93.48%),B组共21人看到眼底(45.65%),两组组间差为47.83%,95%置信区间为29.59%～66.07%(P<0.001)。A组看清眼底的中位识别时间为29 s; 95%置信区间为23～45 s。由于对照组的识别率有限,无法估计中位时间,但是,基于其95%置信区间下限估计,至少需要80 s以上。对数秩检验的P值<0.001,A组明显优于B组。结论这种3D打印的眼模型显著提高了学生的学习兴趣、学习效率及学习效果,值得推广。