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早期在线关注度可预测泌尿科出版物的引用次数:#UroSoMe_Score。

Early Online Attention Can Predict Citation Counts for Urological Publications: The #UroSoMe_Score.

机构信息

Department of Urology, University of Minnesota, Minneapolis, MN, USA.

Department of Urology, University of Minnesota, Minneapolis, MN, USA.

出版信息

Eur Urol Focus. 2020 May 15;6(3):458-462. doi: 10.1016/j.euf.2019.10.015. Epub 2019 Nov 6.

Abstract

BACKGROUND

The scientific impact of published articles has traditionally been measured as citation counts. However, there has been a shift in academia to a digitalized age in which research is widely read, disseminated, and discussed online. As part of this shift, each published article has a digital footprint.

OBJECTIVE

To develop a urology social media score (#UroSoMe_Score) to predict citation counts from measures of online attention for urological articles.

DESIGN, SETTING, AND PARTICIPANTS: We included articles published between June 2016 and June 2017 in the top ten highest-impact urology journals. We obtained data on the online attention received by each of these articles from Altmetric Explorer and 2-yr citation counts from Scopus.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We created a multivariable linear model using the forward stepwise regression method based on the Akaike information criterion to determine the best-fitting model using online sources of attention to predict 2-yr citation count.

RESULTS AND LIMITATIONS

We included a total of 2033 urology articles. The median weighted Altmetric score for the articles included was 4 (interquartile range [IQR] 2-11). The median number of citations for all articles included was 7 (IQR 3-14). There was an association between Altmetric score and 2-yr Scopus citation count (p < 0.001) but the adjusted R value for this model was only 0.013. Our stepwise regression model revealed that citations could be predicted from a model comprising the following sources of online attention: policy documents, Google+, blogs, videos, Wikipedia, Twitter, and Q&A. The adjusted R value for the #UroSoMe_Score model was 0.14, which is superior to the full Altmetric score.

CONCLUSIONS

The #UroSoMe_Score can be used to predict 2-yr citation counts for urological publications on the basis of online metrics.

PATIENT SUMMARY

Online measures of attention can be used to predict citation counts and thus the scientific impact of an article. Our #UroSoMe_Score can be used in such a manner specifically for the urological literature. Outliers may still be present especially for popular topics that receive online attention but are not heavily cited.

摘要

背景

发表文章的科学影响力传统上是通过被引次数来衡量的。然而,学术界已经向数字化时代转变,研究成果在网上被广泛阅读、传播和讨论。随着这一转变,每一篇发表的文章都有数字足迹。

目的

开发一种泌尿科社交媒体评分(#UroSoMe_Score),以根据泌尿科文章的在线关注度来预测被引次数。

设计、设置和参与者:我们纳入了 2016 年 6 月至 2017 年 6 月期间发表在十大最高影响力泌尿科期刊上的文章。我们从 Altmetric Explorer 获得了这些文章的在线关注度数据,从 Scopus 获得了 2 年的被引次数。

结果测量和统计分析

我们使用基于赤池信息量准则的逐步向前回归法创建了一个多变量线性模型,以确定使用在线关注度来预测 2 年被引次数的最佳拟合模型。

结果和局限性

我们共纳入了 2033 篇泌尿科文章。纳入文章的加权平均 Altmetric 评分为 4 分(四分位距[IQR]2-11)。所有文章的中位数被引次数为 7 次(IQR 3-14)。Altmetric 评分与 2 年 Scopus 被引次数之间存在关联(p<0.001),但该模型的调整后的 R 值仅为 0.013。我们的逐步回归模型显示,从以下在线关注度来源的模型中可以预测被引次数:政策文件、Google+、博客、视频、维基百科、Twitter 和问答。#UroSoMe_Score 模型的调整后的 R 值为 0.14,优于完整的 Altmetric 评分。

结论

基于在线指标,#UroSoMe_Score 可用于预测泌尿科出版物的 2 年被引次数,从而预测文章的科学影响力。我们的#UroSoMe_Score 可以专门用于泌尿科文献。对于那些在线关注度高但被引次数不多的热门话题,仍然可能存在异常值。

患者总结

在线关注度可以用于预测文章的被引次数,从而预测文章的科学影响力。我们的#UroSoMe_Score 可以专门用于泌尿科文献。对于那些在线关注度高但被引次数不多的热门话题,仍然可能存在异常值。

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