基于文献的老年人群常见证候分布特征研究＊

目的 了解我国老年人中医证候分布特征，为中医药辨治老年人提供参考依据。方法 系统检索中国知识基础设施（CNKI）、中文科技期刊数据库（CCD）、万方数据资源系统数据库（CSPD），纳入研究对象为老年人的临床流行病学调查研究文献，对其调查目的、调查疾病及调查结果等进行描述统计。结果 ①最终纳入168篇文献，其中2010至2020年之间发表138篇（82.14%）；调查地区涵盖全国28个省市自治区，共纳入43948例老年人，累计覆盖340个研究中心；②主要研究结果显示，老年人中医证候阴虚阳亢证（10.05%）>血瘀证（9.5%）>痰浊（湿）证（8.91%）>阴阳两虚证（4.98%）>痰瘀互结证（4.96%）；单元证分布阴虚证（14.70%）>痰证（11.22%）>气虚证（7.15%）>肾虚证（4.72%）>血瘀证（4.18%）；涉及病变脏腑肾>肝>脾>肺>心。结论 根据统计结果，可以看出老年人证候分布虚证多于实证，虚证中又以阴虚证、气虚证最为多见，实证常见血瘀证、痰浊（湿）证等，且多见痰瘀互结证；老年人五脏皆损，其中又以肾、肝、脾功能失调最为突出。     

补充与替代疗法治疗肠易激综合征的研究进展

肠易激综合征是临床常见的功能性胃肠病之一。由于多数患者的症状经过一线药物治疗后仍不能得到较好改善，许多患者为获得更好的治疗效果，转向选择补充与替代疗法进行治疗。然而，由于研究质量和数量的限制，大部分治疗肠易激综合征的补充与替代疗法并未被相关共识和指南所推荐。本文从补充与替代疗法的分类入手，从天然产品、身心治疗、传统医药3个方面就目前国内外常用的补充与替代疗法治疗肠易激综合征的研究进行概述，以期为临床工作者和患者更好地了解和应用提供帮助。     

基于CiteSpace可视化分析代谢组学在中医药领域的研究现状及趋势＊

目的 应用可视化方法分析代谢组学在中医药领域的现状及趋势。方法 检索中国知网数据库（CNKI）和Web of Science 核心合集数据库2021年3月15日之前收录的中医药领域代谢组学研究的相关文献，应用CiteSpace软件对纳入的文献进行关键词、作者、研究机构等内容进行可视化分析。结果 共纳入中文文献247篇，英文文献350篇。文献数量在波动中迅速上升。中、英文文献作者合作网络显示，张爱华是中医药领域代谢组学研究发文量最多的作者，并形成了核心研究团队。发文机构显示，中国医学科学院是该领域的重要研究机构，机构间合作紧密。中、英文文献关键词分析显示，研究内容主要集中在核磁共振、代谢标记物、冠心病、质谱技术、代谢通路等相关领域。结论 中医药领域代谢组学研究的热点主要为中医药治疗代谢性疾病的机制研究。研究趋势为卵泡代谢组学研究及中药有效成分的研究。     

A meta-analysis for Lianhua Qingwen on the treatment of Coronavirus disease 2019 (COVID-19)

BackgroundTraditional Chinese medicine (TCM) has been proven to played a great important role on the treatment of COVID-19. As one of the drugs recommended in Chinese guidelines, Lianhua Qingwen Granules or Capsules (LQ) are widely used.This systematic review and meta-analysis amis to evaluate the clinical efficacy of LQ on the treatment of COVID-19.MethodsSeven databases (PubMed, EMBASE, CENTRAL, CNKI, VIP, CBM and Wanfang) were searched to include all appropriate clinical trials that explore the efficacy of LQ on the treatment of COVID-19.ResultA total of 3 trials including 245 COVID-19 patients were eventually enrolled.Compared with the control group,the LQ group showed great significant difference on reducing the rate of clinical change to severe or critical condition[RR = 0.38, 95 %CI (0.17,0.85), P < 0.05]and the fever time (SMD =-0.57,95 %CI (-0.96,-0.17), P<0.05),as well as the significant improvement on the disappearance rate of the clinical symptoms: fever [RR = 1.36,95 %CI (1.14,1.61), P < 0.05],cough[RR = 1.99,95 %CI (1.39,2.86), P < 0.05],fatigue[RR = 1.52,95 %CI (1.15,2.01), P < 0.05] and anhelation [RR = 4.18,95 %CI (1.99,8.81), P < 0.05], but no significance on expectoration[RR = 2.46,95 %CI (0.81,7.51), P < 0.05].ConclusionThe clinical application of LQ on the treatment of COVID-19 has significant efficacy in improving clinical symptoms and reducing the rate of clinical change to severe or critical condition. Nevertheless, due to the limited quantity and quality of the included studies, more and higher quality trials with more observational indicators are expected to be published.     

Emergency Care EMTALA Alterations During the COVID-19 Pandemic in the United States

The coronavirus 2019 pandemic has affected almost every aspect of health care delivery in the United States, and the emergency medicine system has been hit particularly hard while dealing with this public health crisis. In an unprecedented time in our history, medical systems and clinicians have been asked to be creative, flexible, and innovative, all while continuing to uphold the important standards in the US health care system. To continue providing quality services to patients during this extraordinary time, care providers, organizations, administrators, and insurers have needed to alter longstanding models and procedures to respond to the dynamics of a pandemic. The Emergency Medicine Treatment and Active Labor Act of 1986, or EMTALA, is 1 example of where these alterations have allowed health care facilities and clinicians to continue their work of caring for patients while protecting both the patients and the clinicians themselves from infectious exposures at the same time.     

人工智能在中医药领域的应用进展及现状思考

人工智能（Artificial Intelligence，AI）的蓬勃兴起为现代社会带来了前所未有的机遇，中医药是中华民族传承千年的文化瑰宝。随着人工智能技术不断在中医药领域的科技创新中崭露头角，二者的融合不断加深，人工智能在中医药领域的发展前景、争议挑战也引发了诸多思考。本文将从人工智能在中医药领域的应用入手，对人工智能辅助中医诊断、智能决策与数据挖掘、健康管理及中草药现代化研究等方面，就近年来国内外研究进展进行总结与分析，以期为AI视域下实现中医药现代化、智能化赋能。     

我国家庭医生签约服务政策执行的制约因素与优化路径：基于史密斯政策执行过程模型

深化家庭医生签约服务是深化医药卫生体制改革、强化基层医疗卫生服务、实现"健康中国"战略目标的重要选择，也是当前更好维护人民群众健康的重要途径。为有效推进签约服务工作，国家陆续推出各项政策，全国各地也在积极进行实践探索，成效明显。但是，签约服务仍面临诸多问题，其中"执行难"是签约服务深度推进的一大困境。通过史密斯政策执行过程模型，结合签约服务政策执行过程，发现签约服务仍存在法治性不足、政策执行人员水平不高、激励不足、政策环境影响等诸多制约因素。因此，需要从法律和制度方面进行顶层设计、提升执行人员素质和职业认同、建立医患互信、优化政策执行环境等角度进行政策创新，探索家庭医生签约服务可持续发展的路径。     

The success of Australia’s ‘No Jab,No Pay’ policy at a local level; retrospective clinical audit of a single medical practice assessing incidence of catch-up vaccinations

Vaccination is a vital health care initiative to prevent individual and population infection. To increase vaccination rates the federal government implemented the ‘No Jab, No Pay’ policy, where eligibility for several government benefits required children to be fully vaccinated by removing ‘conscientious objections’ and expanding the age range of children whose families receive benefits. This study assesses the impact of this policy at a local area within a single medical practice community in NSW, Australia. A retrospective clinical audit was performed between 2012 and 2017 on a single general practice's vaccination records for children ≤19 years. Catch-up vaccinations were assessed based on age at vaccination. Incidence of catch-up vaccinations was assessed for each of four years before and two years after the implementation of the ‘No Jab, No Pay’ policy in January 2016, along with the age of children and vaccination(s) given. Catch-up vaccinations were assessed temporally either side of implementation of ‘No Jab, No Pay’. Comparing the average annual vaccination catch-up incidence rate of 6.2% pre-implementation (2012–2015), there was an increase to 9.2% in 2016 (p < .001) and 7.8% in 2017 (p = .027). Secondary outcome measurement of catch-up vaccination incidence rates before (2012–2015) and after (2016–2017) ‘No Jab, No Pay’ implementation showed statistically significant increases for children aged 8–11 years (3.2%–5.6%, p = .038), 12–15 years (7.5%–14.7%, p < .001) and 16–19 years (3.3%–10.2%, p < .001) along with a statistically significant reduction in children aged 1–3 years (11.4%–6.2%, p = .015). Also, catch-up rates for DTPa significantly increased after program implementation. This study demonstrates that the Australian federal government vaccination policy ‘No Jab, No Pay’ was coincident with an increase in catch-up vaccinations within a rural NSW community served by one medical practice, especially for older children.