采集5397篇引用Hirsch J E(2005)的文献,数据来源于Web Of Science核心合集数据库。从文献的时间、空间、作者、主题及关联文献的协同扩散剖析。发现①论文知识得到不断扩散,扩散范围有美国、中国、西班牙、英国和德国等国家;②机构有格拉纳达大学、智利大学、鲁汶大学、安特卫普大学和中国科学院等学术机构;③论文引用者包含Xia,Feng、Ding,Ying、Radicchi,Filippo、Bu,Yi及Waltman,Ludo等;④近期扩散到的主题有替代计量学、知识图谱和研究趋势等知识可视化的应用。
Background:The relationship between perfusion index(PI)and organ dysfunction in patients in the intensivecare unit(ICU)is not clear.This study aimed to explore the relationship between PI and renal function in theperioperative critical care setting and evaluate the predictive efficiency of PI on patients with acute kidney injury(AKI)in the ICU.Methods:This retrospective analysis involved 12,979 patients who had undergone an operation and were admitted to the ICU in Peking Union Medical College Hospital from January 2014 to December 2019.The distributionof average PI in the first 24 h after ICU admission and its correlation with AKI was calculated by Cox regression.Receiver operating characteristic(ROC)curves were generated to compare the ability of PI,mean arterial pressure(MAP),creatinine,blood urea nitrogen(BUN),and central venous pressure(CVP)to discriminate AKI in thefirst 48 h in all perioperative critically ill patients.Results:Average PI in the first 24 h served as an independent protective factor of AKI(Odds ratio[OR]=0.786,95%confidence interval[CI]:0.704–0.873,P<0.0001).With a decrease in PI by one unit,the incidence of AKIincreased 1.74 times.Among the variables explored for the prediction of AKI(PI,MAP,creatine,BUN,and CVP),PI yielded the highest area under the ROC curve,with a sensitivity of 64.34%and specificity of 70.14%.A cut-offvalue of PI≤2.12 could be used to predict AKI according to the Youden index.Moreover,patients in the low PIgroup(PI≤2.12)exhibited a marked creatine elevation at 24–48 h with a slower decrease compared with thosein the high PI group(PI>2.12).Conclusions:As a local blood flow indicator,the initial 24-h average PI for perioperative critically ill patients canpredict AKI during their first 120 h in the ICU.
Shengjun LiuLongxiang SuChangjing ZhugeHuaiwu HeYun Long
针对数字文献资源利用效益评价问题,利用情报学中H指数的基本逻辑内涵和影响因子的算法思想,提出了一种从引文产出角度对文献资源评价的新方法。构建了资源下载H指数HOD(H-index of Download)和资源引用H指数HOC(H-index of citation),并以此为基础进一步提出了资源使用效益H指数HOB(H-index of Benefits)。在实证研究中,统计了5所重点高校3种数字文献资源的HOD、HOC和HOB指数值,并对比传统的资源下载量,对比不同大学之间的HOB与数据资源之间的HOB指数,从而证明了HOB指数具有较好的资源效益产出评价的稳定性和敏感性,打破了以下载量作为评价指标的传统思维。从文献资源产出效益和效率的角度更为科学地衡量文献资源的利用真实效果。
