工作坊模式在护生压疮护理带教中的应用效果分析

目的探讨工作坊模式在护生压疮护理带教中的应用效果,为临床护理带教提供参考。方法选取2018年7月—2019年6月在医院实习的120名护生作为研究对象,随机分为对照组和观察组,各60名护生。对照组采用传统带教法进行压疮培训,观察组采用工作坊教学模式进行培训。培训结束后比较两组护生压疮知识的掌握程度即理论考核、技能操作成绩,教学效果满意度。结果观察组护生对压疮知识的掌握程度即理论考核成绩、技能操作考核成绩远远高于对照组,两组差异具有统计学意义(P<0.05)。95%的观察组护生对工作坊教学模式的效果表示满意,认为工作坊教学模式能够提高学习的兴趣、主动性和临床实践能力,对临床工作有很大帮助。结论工作坊教学模式应用于压疮护理的临床带教,能让护生亲身体验压疮护理的过程,并参与其中,将理论与实践相结合,更好地活跃课堂气氛,启发护生思考,提高护生对压疮理论和技能的掌握,为临床实际工作中压疮护理质量的提高奠定基础。     

肝癌介入术治疗患者术中应用心理护理的效果

目的研究在肝癌介入术治疗患者中应用心理护理干预的临床价值。方法本文数据计算目标是2018年1月-2019年6月的60例肝癌介入术治疗患者,以随机数字表法的形式进行分组研究,常规组(n=30)开展一般护理干预,心理组(n=30)开展心理护理干预,比较心理组与常规组肝癌介入术治疗患者临床护理情况。结果心理组肝癌介入术治疗患者治疗后临床护理满意度、焦虑评分、抑郁评分与常规组比较,两组治疗后肝癌介入术治疗患者焦虑评分、抑郁评分与治疗前比较,P<0.05,差异有统计学意义。结论将心理护理干预应用在肝癌介入术治疗患者中可提升护理满意度。     

抑郁症康复技能训练程式在基层医院推广应用的价值研究

背景　抑郁症的康复技能训练是一种很好的辅助治疗方法，本课题通过引进成熟的抑郁症康复技术，使基层医务人员熟练掌握，为辖区内广大抑郁症患者开展康复训练，以期提高抑郁症患者的疗效，降低自杀风险。目的　探讨抑郁症康复技能训练程式在基层医院推广应用的价值。方法　选取门头沟区龙泉医院2018年1-9月门诊就诊的65例抑郁症患者为研究对象。入选患者全部接受康复训练，分别于训练前和训练结束3个月后采用汉密尔顿抑郁量表（HAMD）、社会功能缺陷筛选量表（SDSS）、服药依从性、自杀风险评估量表（nurses' global assessment of suicide risk，NGASR）和自杀风险问卷对患者进行评估。结果　共61例患者完成研究，与训练前比较，训练结束3个月后患者HAMD、SDSS评分明显降低，服药依从性除自行停药外均明显提高，自杀风险明显降低，差异有统计学意义（P<0.05）。结论　抑郁症康复训练程式能有效提高患者服药依从性，改善抑郁症状，减少自杀观念，在基层医院有推广价值。     

江苏省儿童专科夜间门诊诊察费定价政策成效分析

2019年5月20日,江苏省医保局推出调整了儿童专科夜间门诊诊察费收费标准举措。文章以江苏省某三甲综合医院为样本,围绕上述政策实施后儿科诊疗工作量、病人满意度、医院收入影响等方面进行了调查,结果表明,上述政策可满足儿科病人就诊差异化需求,合理分流病人,保障急诊优先的诊疗秩序。     

益胃汤加味方对原发性干燥综合征疲劳症状的影响

目的:探讨益胃汤加味对改善原发性干燥综合征(primary sj?rgren's syndrome,pSS)气阴两虚、瘀血阻络证患者疲劳症状及免疫学指标的治疗效果。方法:将116例原发性干燥综合征患者按照随机、对照、单盲法分为治疗组与对照组,每组58例。治疗组给予益胃汤加味方,每日1剂,2次/d;对照组给予硫酸羟氯喹200 mg/次,2次/d,两组均连续治疗24周。比较治疗前后两组患者中医证候疗效、中医疲劳症状积分、疲劳症状视觉模拟评分(visual analogue scale,VAS)以及类风湿因子(rheumatoid factor,RF)与血清免疫球蛋白G(immunoglobulin G,IgG)水平。结果:治疗后治疗组患者中医证候疗效优于对照组(Z=3. 712,P0. 05);治疗组中医疲劳积分与疲劳症状视觉模拟评分改善均优于对照组(P0. 05)。治疗6个月后,治疗组IgG水平较本组治疗前明显下降(P0. 05),与对照组组间比较差异无统计学意义。治疗后两组类风湿因子与本组治疗前比较均无明显下降。治疗组不良反应率1. 75%(1/57),对照组不良反应发生率为25. 49%(13/51)。结论:与硫酸羟氯喹相比,益胃汤加味方治疗pSS,可明显改善患者疲劳及总体症状,两者均可降低血清IgG,益胃汤加味方组疗效较硫酸羟氯喹更明显。两组药物对pSS患者RF水平均无明显影响。长期用药,益胃汤加味方治疗pSS较硫酸羟氯喹更为安全。     

Subscapularis Transthoracic Versus Posterolateral Approaches in the Surgical Management of Upper Thoracic Tuberculosis: A Prospective,Randomized Controlled Study

The objective of the present study was to evaluate the clinical, radiological, and functional outcomes of a subscapularis transthoracic surgical approach and a posterolateral surgical approach with debridement, bone graft fusion, and internal fixation for the treatment of upper thoracic tuberculosis.There is currently debate over the best surgical approach for the treatment of upper thoracic tuberculosis. Traditionally, the subscapularis transthoracic approach has been preferred; however, the posterolateral approach has gained popularity in the past few years.A prospective, consecutive cohort of 43 upper thoracic tuberculosis patients with a mean age of 39 years (range: 20–52 years) was followed up for a minimum of 12 months (range: 12–60 months). Patients were randomly divided into 2 groups. Group A (n = 21) was treated by the subscapularis transthoracic approach and group B (n = 22) was treated by the posterolateral approach. All cases were evaluated for clinical, radiological, and functional outcomes. Intraoperative blood loss, operative duration, intraoperative and postoperative complications, hospital stay, the cure rate, fusion time, and the Frankel scale were used for clinical and functional evaluation, whereas the kyphosis angle was used for radiological evaluation.Grafted bones were fused by 10 months in all cases. There was no statistically significant difference between groups before surgery in terms of gender, age, segmental tuberculosis, erythrocyte sedimentation rate (ESR), Frankel scale, or Cobb''s angle (P > 0.05). The average operative duration for Group B was lower than that of Group A. There were no significant differences in intraoperative blood loss, intraoperative and postoperative complications, hospital stay, grafted bone fusion time, or cure rate between groups (P > 0.05). The Cobb''s angle correction rate for group B (68.5%) was significantly better than that of group A (30.9%). The neurological score showed significant postoperative improvement in both groups, with no significant difference between the groups.The subscapularis transthoracic approach and the posterolateral approach with debridement, bone graft fusion, and internal fixation are both sufficient and satisfactory for the surgical treatment of upper thoracic tuberculosis. However, the posterolateral approach is superior to the subscapularis transthoracic approach in terms of surgical trauma, operative time, and kyphosis correction.     

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.