发达国家医学生器官捐献意愿研究现状的范围综述

目的了解发达国家医学生器官捐献意愿研究的目的、结果、障碍因素和解决对策。 方法以主题词和自由词联合的方式对PubMed、Web of Science、Ovid MEDLINE、Ovid EMBASE(Excerpta Medica)、EBSCOhost Complete和Cochrane Library等已发表文献数据库，以及灰色文献数据库York Centre for Reviews and Dissemination、Open Grey、ProQuest Dissertation and Theses和Mednar进行系统、全面地检索，并采用Arksey和O′Malley提出的范围综述框架进行文献资料整合。 结果共纳入17项发达国家医学生器官捐献意愿研究。研究背景主要为医学生未来对于公众器官捐献有着重要影响。研究结果表明，大多数医学生器官捐献意愿性良好，但不同亚组的研究结果不一；其最主要的障碍因素为医学生器官捐献相关知识的缺乏；解决对策主要为施予系统化、结构化和可评价的教育课程。 结论21世纪以来发达国家医学生器官捐献意愿研究方兴未艾，其研究的多层面、多角度以及揭示的问题、寻求的解决对策，可对我国现阶段医学生器官捐献的研究提供一定参考。     

Medical error disclosure in the Italian healthcare context: A delicate balance between ethical obligations and the principle of non-self-incrimination

Surprisingly, Italian legal doctrine and jurisprudence never systematically address the medical error disclosure issue. The topic of medical error communication represents a non-negligible profile of interest, relating to the possible detrimental effects on doctors who accuse themselves of conduct that does not comply with the lex artis. The effects of error disclosure on the effectiveness of insurance guarantees in civil and administrative liability are particularly relevant, as are the implications for ethical liability. Although the burden of reporting an error falls within the wide range of informative duties doctors hold, it still seems far from having found a factual statement in clinical practice, especially in Italy. This applies whether the error has a marginal or significant impact on the patient’s health. The reasons lay in a very contradictory legal framework. Doctors tend with increasing ease not to comply with their information obligation – especially in cases where fulfilling this duty means admitting a personal, professional error – to preserve the integrity of their professional images. This article aims to offer a brief overview of that topic in the context of Italian healthcare.     

近端胃切除人工三角瓣成形抗反流术的力学机理仿真分析

目的:对食管胃结合部腺癌近端胃切除术（PG）人工三角瓣成形后残胃食糜进行流体动力学数值模拟，并计算不同性质胃内食糜的流动特征。方法:构建常规PG和人工三角瓣成形术术后胃仿真模型，运用Fluent软件对不同粘度胃内食糜反流问题进行数值模拟。结果:站立位姿态时，相对常规PG方案，人工三角瓣成形手术方案表现出较好的抗反流作用；卧位姿态时，当胃内食糜粘度大于0.145 2 Pa[?s，且胃内食糜不超过人工三角瓣情况下，人工三角瓣成形抗反流手术表现出较好的抗反流效果；人工三角瓣抗反流成形手术方案数值模拟结果与临床上患者表现一致。结论:本研究仿真分析为人工三角瓣成形抗反流手术方案的有效性机理分析、临床患者术后饮食及手术方案的进一步改进提供理论及数值依据。 【关键词】食管胃结合部腺癌；抗反流；人工三角瓣；计算流体力学     

我国三级医疗机构营养科专业人才队伍情况调查

目的调查分析我国三级医疗机构营养科专业人才队伍情况，提出针对性建议。方法从国家医疗质量管理与控制信息网选取1 283家三级医疗机构调研临床营养专业技术人员数据，应用频数、构成比等进行描述性统计分析。 结果三级医疗机构营养科专业技术人员数量不足且分布不均衡，高层次人才比例偏低。医床比1:556.91，技床比1:841.30，医技比1.51:1，护床比1:1 031.31，均未达到文件标准，营养科医师严重缺编。结论应加强营养科专业人员教育和高层次人才培养，建立规范化培养体系，明确营养科职称评定和岗位技能考核制度，不断扩增专业人才数量，提升临床营养诊疗能力。     

Development of deep learning segmentation models for coronary X-ray angiography: Quality assessment by a new global segmentation score and comparison with human performance

Introduction and objectivesAlthough automatic artificial intelligence (AI) coronary angiography (CAG) segmentation is arguably the first step toward future clinical application, it is underexplored. We aimed to (1) develop AI models for CAG segmentation and (2) assess the results using similarity scores and a set of criteria defined by expert physicians.MethodsPatients undergoing CAG were randomly selected in a retrospective study at a single center. Per incidence, an ideal frame was segmented, forming a baseline human dataset (BH), used for training a baseline AI model (BAI). Enhanced human segmentation (EH) was created by combining the best of both. An enhanced AI model (EAI) was trained using the EH. Results were assessed by experts using 11 weighted criteria, combined into a Global Segmentation Score (GSS: 0–100 points). Generalized Dice Score (GDS) and Dice Similarity Coefficient (DSC) were also used for AI models assessment.Results1664 processed images were generated. GSS for BH, EH, BAI and EAI were 96.9+/-5.7; 98.9+/-3.1; 86.1+/-10.1 and 90+/-7.6, respectively (95% confidence interval, p<0.001 for both paired and global differences). The GDS for the BAI and EAI was 0.9234±0.0361 and 0.9348±0.0284, respectively. The DSC for the coronary tree was 0.8904±0.0464 and 0.9134±0.0410 for the BAI and EAI, respectively. The EAI outperformed the BAI in all coronary segmentation tasks, but performed less well in some catheter segmentation tasks.ConclusionsWe successfully developed AI models capable of CAG segmentation, with good performance as assessed by all scores.     

Registries,Databases and Repositories for Developing Artificial Intelligence in Cancer Care

Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches.