Kalvapudi Sukumar, Venkatesan Subeikshanan, Belavadi Rishab, Anand Varun, Madhugiri Venkatesh S
Neurosurgery, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Pondicherry, IND.
Surgery, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Pondicherry, IND.
Cureus. 2022 Jul 21;14(7):e27111. doi: 10.7759/cureus.27111. eCollection 2022 Jul.
Background and objective There is a paucity of information regarding the concordance of traditional metrics across publicly searchable databases and about the correlation between alternative and traditional metrics for neurosurgical authors. In this study, we aimed to assess the congruence between traditional metrics reported across Google Scholar (GS), Scopus (Sc), and ResearchGate (RG). We also aimed to establish the mathematical correlation between traditional metrics and alternative metrics provided by ResearchGate. Methods Author names listed on papers published in the Journal of Neurosurgery (JNS) in 2019 were collated. Traditional metrics [number of publications (NP), number of citations (NC), and author H-indices (AHi)] and alternative metrics (RG score, Research Interest score, etc. from RG and the GS i10-index) were also collected from publicly searchable author profiles. The concordance between the traditional metrics across the three databases was assessed using the intraclass correlation coefficient and Bland-Altman (BA) plots. The mathematical relation between the traditional and alternative metrics was analyzed. Results The AHi showed excellent agreement across the three databases studied. The level of agreement for NP and NC was good at lower median counts. At higher median counts, we found an increase in disagreement, especially for NP. The RG score, number of followers on RG, and Research Interest score independently predicted NC and AHi with a reasonable degree of accuracy. Conclusions A composite author-level matrix with AHi, RG score, Research Interest score, and the number of RG followers could be used to generate an "Impact Matrix" to describe the scholarly and real-world impact of a clinician's work.
背景与目的 关于可公开搜索数据库中传统指标的一致性，以及神经外科作者的替代指标与传统指标之间的相关性，相关信息匮乏。在本研究中，我们旨在评估谷歌学术（GS）、Scopus（Sc）和ResearchGate（RG）所报告的传统指标之间的一致性。我们还旨在建立传统指标与ResearchGate提供的替代指标之间的数学相关性。方法 整理了2019年发表在《神经外科杂志》（JNS）上的论文所列出的作者姓名。还从可公开搜索的作者简介中收集了传统指标[发表论文数量（NP）、被引次数（NC）和作者H指数（AHi）]以及替代指标（RG得分、RG的研究兴趣得分等以及GS的i10指数）。使用组内相关系数和布兰德-奥特曼（BA）图评估三个数据库中传统指标之间的一致性。分析传统指标与替代指标之间的数学关系。结果 在研究的三个数据库中，AHi显示出极好的一致性。在较低的中位数计数时，NP和NC的一致性水平良好。在较高的中位数计数时，我们发现不一致性增加，尤其是对于NP。RG得分、RG上的关注者数量和研究兴趣得分能以合理的准确度独立预测NC和AHi。结论 一个包含AHi、RG得分、研究兴趣得分和RG关注者数量的综合作者层面矩阵可用于生成一个“影响力矩阵”，以描述临床医生工作的学术和现实世界影响力。
