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Big Data Research in Pediatric Neurosurgery: Content, Statistical Output, and Bibliometric Analysis

机译:儿科神经外科大数据研究:内容,统计输出和义毛管计分析

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Background/Aims: We sought to describe pediatric “big data” publications since 2000, their statistical output, and clinical implications. Methods: We searched 4 major North American neurosurgical journals for articles utilizing non-neurosurgery-specific databases for clinical pediatric neurosurgery research. Articles were analyzed for descriptive and statistical information. We analyzed effect sizes (ESs), confidence intervals (CIs), and p values for clinical relevance. A bibliometric analysis was performed using several key citation metrics. Results: We identified 74 articles, which constituted 1.7% of all pediatric articles ( n = 4,436) published, with an exponential increase after 2013 (53/74, 72%). The Healthcare Cost and Utilization Project (HCUP) databases were most frequently utilized ( n = 33); hydrocephalus ( n = 19) was the most common study topic. The statistical output ( n = 49 studies with 464 ESs, 456 CIs, and 389 p values) demonstrated that the majority of the ESs (253/464, 55%) were categorized as small; half or more of the CI spread (CIS) values and p values were high (274/456, 60%) and very strong (195/389, 50%), respectively. Associations with a combination of medium-to-large ESs (i.e., magnitude of difference), medium-to-high CISs (i.e., precision), and strong-to-very strong p values comprised only 20% (75/381) of the reported ESs. The total number of citations for the 74 articles was 1,115 (range per article, 0–129), with the median number of citations per article being 8.5. Four studies had > 50 citations, and 2 of them had > 100 citations. The calculated h -index was 16, h -core citations were 718, the e -index was 21.5, and the Google i10-index was 34. Conclusions: There has been a dramatic increase in the use of “big data” in the pediatric neurosurgical literature. Reported associations that may, as a group, be of greatest interest to practitioners represented only 20% of the total output from these publications. Citations were weighted towards a few highly cited publications.
机译:背景/宗旨:我们试图自2000年以来描述儿科“大数据”出版物,统计产出和临床意义。方法:采用非神经外科专用数据库,搜查了4个主要的北美神经外科期刊,用于临床小儿神经外科研究。分析了描述性和统计信息的文章。我们分析了临床相关性的效果大小(ESS),置信区间(CIS)和P值。使用几个关键引用指标进行学生分析。结果:我们确定了74篇文章,其占所有儿科文章(N = 4,436)的1.7%,2013年后的指数增加(53/74,72%)。医疗保健成本和利用率项目(HCUP)数据库最常使用(n = 33);脑积水(n = 19)是最常见的研究主题。统计产出(N = 49研究,464个ESS,456个CI和389页值)表明大多数ESS(253/464,55%)分为小; CI扩散(CIS)值和P值的一半以上分别为高(274/456,60%),分别非常强(195/389,50%)。与中大ESS的组合(即差异,差异的大小),中高的CISS(即精度)和强度为非常强的P值仅占20%(75/381)的相关的关联报告的ESS。 74条文章的总数为1,115(每篇文章的范围0-129),每个文章的中位数为8.5。四项研究> 50个引文,其中2个引用> 100个引文。计算出的H -Index为16,H-CORE引文为718,E -Index为21.5,谷歌I10指数为34.结论:在儿科的“大数据”中使用了急剧增加神经外科文献。报告的协会可以作为一群人对从业者的最大兴趣达到这些出版物的20%。引用引用的引用率为一些高度引用的出版物。

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