牙支持3D导板辅助穿刺卵圆孔射频热凝治疗三叉神经痛的疗效分析

目的:探讨牙支持式3D打印导板辅助穿刺卵圆孔射频热凝治疗三叉神经痛术中术后的疗效分析.方法:对2019年1月～2021年5月收治的三叉神经痛患者40人,根据患者需要随机分成2组:A组(对照组),常规按照Hartel前入路法穿刺卵圆孔,进入卵圆孔后,耳-床线定位确定入颅深度后行射频治疗.B组(导板组),采用3D导板,给患者戴上导板固定后,完成穿刺,耳-床线定位确定入颅深度后行射频治疗.分析2组的术前疼痛评分(VAS)术后即刻及术后1月疼痛消失的有效率,手术时间,穿刺次数,并发症,穿刺的偏移程度.结果:与对照组A组比较在术后即刻有效率、术后1月随访有效率比较无统计学差异(P>0.05).在穿刺次数、穿刺偏移率、手术时间、并发症两组间比较有统计学差异(P<0.05).导板与牙齿紧密贴合率100％.结论:3D打印导板应用于穿刺卵圆孔具有明显的优势,可以减少患者创伤,减少X线辐射,减少穿刺偏移,术后疗效肯定,值得广泛应用.     

基于标准化患者法评价四川农村基层医生两种慢性病诊断准确性及其影响因素

目的 运用标准化患者法评估四川农村地区基层医生不稳定型心绞痛和2型糖尿病两种慢性病诊断准确性现状,探讨基层医生两种慢性病诊断准确性的主要影响因素,为提升基层医生两种慢性病诊断准确性提供科学依据。方法 采用多阶段随机整群抽样方法,抽取四川省自贡市5个区/县50个乡镇100个村为研究现场,以调查当日在岗的全科及内科医生作为研究对象。共进行两轮数据采集,第1轮采集样本乡镇卫生院和村卫生室医生的基本信息;第1轮调查完成1个月后,运用标准化患者法开展第2轮调查,收集农村基层医生对不稳定型心绞痛和2型糖尿病诊断结果信息。运用Logistic回归分析农村基层医生不稳定型心绞痛和2型糖尿病诊断准确性的影响因素。结果 共纳入172名农村基层医生,完成186次标准化患者访问,正确诊断率为48.39%。其中不稳定型心绞痛的正确诊断率为18.68%(17/91),2型糖尿病的正确诊断率为76.84%(73/95)。Logistic回归分析显示,具有执业医师资质的农村基层医生更有可能做出正确诊断(OR=4.857,95%CI=1.076~21.933,P=0.040)。农村基层医生在诊断过程中涉及的必要问诊和检查条目越多,做出正确诊断的概率越高(OR=1.627,95%CI=1.065~2.485,P=0.024)。与不稳定型心绞痛相比,农村基层医生对2型糖尿病做出正确诊断的可能性更高(OR=6.306,95%CI=3.611~11.013,P<0.001)。结论 四川农村基层医生不稳定型心绞痛和2型糖尿病诊断准确性整体较差,建议以基层医生慢性病诊断过程质量改善为突破口,提升基层医生执业水平,进而提高慢性病诊断准确性。     

中医药干预新型冠状病毒肺炎临床研究方案分析

对中医药防治新型冠状病毒肺炎临床研究方案注册情况进行分析,为提高相关研究设计质量提供参考和建议。检索中国临床试验注册中心网站(www.chictr.org.cn)以及美国临床试验注册中心网站(clinicaltrials.gov),以新型冠状病毒肺炎、2019-nCoV等为检索词,检索新型冠状病毒肺炎中医药相关临床研究方案。按照纳入排除标准筛选文献,并提取研究注册时间、研究目的、研究类型、申办单位、研究对象、样本量、干预措施、评价指标等数据,采用描述性分析方法。共纳入新型冠状病毒肺炎中医药相关研究方案49个,研究负责单位以湖北、北京、浙江等地医院或高等院校为主。研究具体实施单位属地集中在湖北、广东、浙江、河南等地医院。研究设计以干预性试验研究为主(共40个),其中随机平行对照研究30个,非随机对照试验7个,单臂研究2个,连续入组1个;观察性研究6个;卫生服务研究2个;预防性研究1个。总样本量30562例,单个研究样本量最大20000例,最小30例。49个方案的研究对象包括健康人群(3个)、隔离观察人群(1个)、疑似病例(10个)、确诊病例(31个)、康复期病例(4个)。31个拟纳入确诊病例的研究方案中,有16个研究未明确病情分级,3个研究明确排除危重症,4个研究纳入普通型,2个研究纳入轻型、普通型或重型,1个研究纳入轻型和普通型,1个研究纳入普通型或重型,3个研究纳入重型,1个研究纳入重症或危重症。评价的干预措施包括中成药(连花清瘟胶囊/颗粒、藿香正气滴丸/口服液、八宝丹、固表解毒灵、金蒿解热颗粒、复方鱼腥草合剂、金叶败毒颗粒、疏风解毒胶囊、双黄连口服液、痰热清注射液、血必净注射液、热毒宁注射液、喜炎平注射液)、汤药、太极拳疗法。主要疗效指标以退热时间、临床症状缓解、新型冠状病毒核酸转阴、重症转化率、胸部CT影像为主。结果表明中医药防治新冠肺炎的临床研究响应快速,当前注册方案涵盖了疾病预防、治疗和康复全过程。但存在人群定义不清,研究目标不明确,干预方案需要细化,疗效评价指标需要优化等问题;另外,需要考虑疫情救治的实际困难和工作负担,在符合医学伦理条件下,优化流程,提高研究方案的可操作性。     

Uncertainty evaluation at three spatial scales for the NDVI-based VWC estimation method used in the SMAP algorithm

Vegetation water content (VWC) is the key input parameter for a soil moisture retrieval algorithm based on microwave remote sensing, and VWC uncertainty can limit the estimated accuracy of soil moisture. There has been little research on VWC algorithm development and validation in China, and the uncertainty of the VWC estimation method has not been well evaluated. Therefore, the aim of this study is to evaluate the uncertainty of the VWC estimation method used in the SMAP (Soil Moisture Active Passive) algorithm on three spatial scales (the point-scale, 30 m scale, and 1 km scale) for maize in northeast China. Results from three ground experimental datasets showed that the SMAP VWC estimation method was strongly biased with an average overestimation of 1.16 kg m^?2,1.04 kg m^?2, and 1.13 kg m^?2 for the point-scale, 30 m scale, and 1 km scale respectively, and maximum bias occurred in the mid-stage of maize. Also, a new power relationship between NDVI (Normalized Difference Vegetation Index) and VWC was proposed for the 30 m scale based on Sentinel 2 NDVI and field VWC values from 2017 experiment, with respective R² (coefficient of determination) and Root Mean Squared Error (RMSE) values of 0.80 and 0.67 kg m^?2. The results confirmed that this power relationship was still suitable for VWC estimation at the 1 km scale, and it has smaller bias than the original SMAP VWC method. Future work will be carried out to evaluate the applicability of this VWC estimation method over a lager region. It is expected that it can improve the accuracy of soil moisture by providing high precision VWC input parameters.     

Exact confidence intervals for the average causal effect on a binary outcome

Based on the physical randomization of completely randomized experiments, in a recent article in Statistics in Medicine, Rigdon and Hudgens propose two approaches to obtaining exact confidence intervals for the average causal effect on a binary outcome. They construct the first confidence interval by combining, with the Bonferroni adjustment, the prediction sets for treatment effects among treatment and control groups, and the second one by inverting a series of randomization tests. With sample size n, their second approach requires performing O(n⁴)randomization tests. We demonstrate that the physical randomization also justifies other ways to constructing exact confidence intervals that are more computationally efficient. By exploiting recent advances in hypergeometric confidence intervals and the stochastic order information of randomization tests, we propose approaches that either do not need to invoke Monte Carlo or require performing at most O(n²)randomization tests. We provide technical details and R code in the Supporting Information . Copyright © 2016 John Wiley & Sons, Ltd.