儿童外科教学方法实践及探讨

目的探讨儿童外科的教学特点及教学方法,提高儿外科医生的总体执业水平,促进儿童外科学的发展。方法通过分析四川大学华西医院多年儿童外科疾病的临床教学实践经验,总结出适合医学生、初级儿童外科专科医生的实用的教学方法及经验。结果结合儿童外科特有的疾病特点所总结出的故事式讲述教学、典型病例教学、漫画形式学习、比较学习法及多媒体素材教学五种方式是非常适合儿童外科教学的方法。能够加深记忆,提高兴趣,轻松快捷,事半功倍的掌握相关医学专业知识。结论独特有趣的儿童外科教学方法可提高医学生及儿童外科初级从业者的学习兴趣、专业知识和执业水平。     

Multidimensional Ultrasound Doppler Signal Analysis for Fetal Activity Monitoring

Fetal activity parameters such as movements, heart rate and the related parameters are essential indicators of fetal wellbeing, and no device provides simultaneous access to and sufficient estimation of all of these parameters to evaluate fetal health. This work was aimed at collecting these parameters to automatically separate healthy from compromised fetuses. To achieve this goal, we first developed a multi-sensor–multi-gate Doppler system. Then we recorded multidimensional Doppler signals and estimated the fetal activity parameters via dedicated signal processing techniques. Finally, we combined these parameters into four sets of parameters (or four hyper-parameters) to determine the set of parameters that is able to separate healthy from other fetuses. To validate our system, a data set consisting of two groups of fetal signals (normal and compromised) was established and provided by physicians. From the estimated parameters, an instantaneous Manning-like score, referred to as the ultrasonic score, was calculated and was used together with movements, heart rate and the associated parameters in a classification process employing the support vector machine method. We investigated the influence of the sets of parameters and evaluated the performance of the support vector machine using the computation of sensibility, specificity, percentage of support vectors and total classification error. The sensitivity of the four sets ranged from 79% to 100%. Specificity was 100% for all sets. The total classification error ranged from 0% to 20%. The percentage of support vectors ranged from 33% to 49%. Overall, the best results were obtained with the set of parameters consisting of fetal movement, short-term variability, long-term variability, deceleration and ultrasound score. The sensitivity, specificity, percentage of support vectors and total classification error of this set were respectively 100%, 100%, 35% and 0%. This indicated our ability to separate the data into two sets (normal fetuses and pathologic fetuses), and the results highlight the excellent match with the clinical classification performed by the physicians. This work indicates the feasibility of detecting compromised fetuses and also represents an interesting method of close fetal monitoring during the entire pregnancy.     

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting‐state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case–control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case–control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.     

Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing

IntroductionThis study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest.Materials and MethodsThis study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition.ResultsThe learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1.DiscussionThe measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them.ConclusionObtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.     

以临床胜任力为导向的CBL在急危重症护理本科教学中的实践

目的　探讨临床胜任力为导向的病例教学（case-based learning，CBL）在急危重症护理学本科教学中的可行性和有效性。方法　将急危重症护理本科生120人随机分为CBL教学组和传统教学组。根据每次实习学生人数，CBL教学组分为数个小组，每小组固定一个带教教师，并以小组为单位进行临床教学实践；传统教学组采用既往的临床实习教学方式，即一个学生固定跟随一个带教教师。采用理论考试、技能考试以及问卷调查等多种考核方式评价两组的教学效果，采用SPSS 21.0软件行t检验和卡方检验。结果　与传统教学组相比，CBL教学组在理论考试[（92.5±3.0） vs. （85.3±3.3）]和技能考试[（93.1±4.5） vs. （88.1±3.4）]方面均更好，且差异有统计学意义（P<0.05）；在对教学满意度调查方面，CBL教学组优于传统教学组。结论　以临床胜任力为导向的CBL教学应用于急危重症护理本科教学是可行的，能有效培养学生的临床思维和提高实际的临床胜任力。     

新型管理模式在护理实习教学管理中的应用探讨

目的探讨在进行护理实习教学管理期间新型管理模式应用可行性。方法选择我院2018年1月-2019年2月102例实习护生作为试验对象;数字奇偶法分组后探究每组教学管理模式;对照组(51名):选择传统护理教学管理模式展开;试验组(51名):选择新型护理教学管理模式展开;比较两组护实习护生行为规范合格率、平均业务学习出勤率以及教学满意度评分结果。结果试验组实习护生规范合格率(98.04%)高于对照组(64.71%)(P<0.05);试验组实习护生平均业务学习出勤率(98.04%)高于对照组(62.75%)(P<0.05);试验组实习护生各项教学满意度评分均高于对照组(P<0.05)。结论医院实习护生在接受新型护理实习教学管理后,对于规范合格率的提升,平均业务学习出勤率的提升以及教学满意度评分的提升,均获得显著效果,最终为医院实习护生的学习效率以及护理安全提升奠定了基础。     

PBL与思维导图相结合教学模式在临床教学中的应用

目的探讨PBL与思维导图相结合教学模式在临床教学中的应用。方法本文研究对象为在我院见习的临床本科生,为大理大学2015级一个班级,共55名学生,数据收集时间为2019年1月;按照授课方式的差异将学生分为实验班与对照班,实验班27名学生,对照班28名学生;对照班实施常规教学法,实验班实施PBL与思维导图相结合教学法。结果实验班教学结束测试得分为(85.60±3.46)分,对照班教学结束测试得分为(77.48±3.12)分,差异具有统计学意义(P<0.05);实验班教学方式满意度评分为(90.35±4.12)分,对照班教学方式满意度评分为(80.25±4.05)分,差异具有统计学意义(P<0.05)。结论在临床教学中采用PBL与思维导图相结合教学模式,能够提高学生的专业知识掌握水平与对教学模式的认可度。     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.