Biomechanical behavior of brain injury caused by sticks using finite element model and Hybrid-III testing

Objective: To study the biomechanical mechanism of head injuries beaten with sticks, which is common in the battery or assaultive cases. Methods: In this study, the Hybrid-III anthropomorphic test device and finite element model (FEM) of the total human model for safety (THUMS) head were used to determine the biomechanical response of head while being beaten with different sticks. Total eight Hybrid-III tests and four finite element simulations were conducted. The contact force, resultant acceleration of head center of gravity, intracranial pressure and von Mises stress were calculated to determine the different biomechanical behavior of head with beaten by different sticks. Results: In Hybrid-III tests, the stick in each group demonstrated the similar kinematic behavior under the same loading condition. The peak values of the resultant acceleration for thick iron stick group, thin iron stick group, thick wooden stick group and thin wooden stick group were 203.4 g, 221.1 g, 170.5 g and 122.2 g respectively. In finite element simulations, positive intracranial pressure was initially observed in the frontal comparing with negative intracranial pressure in the contra-coup site. Subsequently the intracranial pressure in the coup site was decreasing toward negative value while the contra-coup intracranial pressure increasing toward positive values. Conclusions: The results illustrated that the stiffer and larger the stick was, the higher the von Mises stress, contact force and intracranial pressure were. We believed that the results in the Hybrid-III tests and THUMS head simulations for brain injury beaten with sticks could be reliable and useful for better understanding the injury mechanism.     

Implementation of Recommendations for Long-Acting Contraception Among Women Aged 13 to 18 Years in Primary Care

ObjectiveClinical specialty societies recommend long-acting reversible contraceptives (LARCs) as first-line contraception for adolescent women. We evaluated whether a combined educational and process improvement intervention enhanced LARC placement in primary care within an integrated health care system.MethodsThe intervention included journal clubs, live continuing education, point-of-care guidelines, and new patient materials. We conducted a retrospective cohort study across 3 time periods: baseline (January 2013?September 2015), early implementation (October 2015–March 2016), and full implementation (April 2016–June 2017). The primary outcome was the proportion of LARCs placed by primary care clinicians among women aged 13 to 18 years compared with gynecology clinicians.ResultsKaiser Foundation Health Plan of Colorado cared for approximately 20,000 women aged 13 to 18 years in each calendar quarter between 2013 and 2017. Overall, LARC placement increased from 7.0 per 1000 members per quarter at baseline to 13.0 per 1000 during the full intervention. Primary care clinicians placed 6.2% of all LARCs in 2013, increasing to 32.1% by 2017 (P < .001), including 45.5% of contraceptive implants. Clinicians who attended educational sessions were more likely to adopt LARCs than those who did not (17.9% vs 6.4% respectively, P = .009). Neither overall LARC placement rates (relative risk, 1.9; 95% confidence interval, 0.7?5.6) nor contraceptive implant rates (relative risk, 3.0; 95% confidence interval, 0.9?9.8) increased significantly in clinicians who attended educational activities.ConclusionsThis multimodal intervention was associated with increased LARC placement for adolescent women in primary care. The combination of education and process improvement is a promising strategy to promote clinician behavior change.     

基于医院信息系统的乳腺癌临床特征及中西医用药特征分析

目的 解析真实世界中乳腺恶性肿瘤患者的人群特征、诊断特征、中西医用药特征，为乳腺癌的临床防治提供参考。方法 采集2002年2月至2015年5月全国60家三级甲等医 院信息系统（Hospital Information System，HIS）中，出院诊断为“乳腺癌”的患者用药信息，采用SAS9.3统计软件，对人口学信息、诊断信息、医嘱用药信息等进行描述性分析。结果 39798例乳腺癌患者，平均年龄（50.93者，平均年龄）岁；多以门诊入院，入院病情以“一般”为主;合并疾病主要为高血压，骨肿瘤，联用西药以抑制肿瘤细胞增殖、治疗并发症、缓解放化疗不良反应为主；中医辨证以痰瘀互结证，气阴两虚证，肝气淤滞证，脾气亏虚证型最为常见，临床清热解毒剂、益气扶正剂，活血化瘀剂应用较多。结论 乳腺癌中西医结合治疗，联用药物广泛，临床治疗基本符合临床指南。     

探讨替米沙坦对冠心病合并糖尿病肾病患者疗效的影响

目的:探究替米沙坦对冠心病合并糖尿病肾病患者疗效的影响情况。方法:56例探究目标对象均为某院接收的冠心病合并糖尿病肾病患者,挑选时间2018年6月~2019年6月。将"计算机表法"作为分组的参考,分配为参照组(n=28例)执行依那普利治疗,探究组(n=28例)执行替米沙坦治疗。结果:探究组的LVEF、LVEDd、肌酐、24h尿蛋白4项指标与参照组相比,差异有统计学意义(P<0.05);收缩压、舒张压、空腹血糖、餐后2h血糖4项指标与参照组相比,差异没有统计学意义(P>0.05)。结论:冠心病合并糖尿病肾病患者选择替米沙坦治疗后,心室功能的重构以及肾脏预后结局均得到改善,且临床效果比依那普利好,值得借鉴。     

Hypertension is a risk factor for adverse outcomes in patients with coronavirus disease 2019: a cohort study

Abstract

Background

Comorbidities are commonly seen in patients with coronavirus disease 2019 (COVID-19), but the clinical implication is not yet well-delineated. We aim to characterize the prevalence and clinical implications of comorbidities in patients with COVID-19.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.