Muhammad Talal Ibrahim, Hamza Imran, Muhammad Hamza Shuja, Haider Sheraz, Andrew Howard, Shahryar Noordin
{"title":"骨科研究中 Altmetric 关注分数预测因素的文献计量分析：调查在线可见性","authors":"Muhammad Talal Ibrahim, Hamza Imran, Muhammad Hamza Shuja, Haider Sheraz, Andrew Howard, Shahryar Noordin","doi":"10.3928/01477447-20240809-03","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Altmetric Attention Score (AAS) captures online attention received by a research article in addition to traditional bibliometrics. We present a comprehensive bibliometric analysis of high AAS articles and identify predictors of AAS in orthopedics.</p><p><strong>Materials and methods: </strong>The top 30 articles with highest AAS were selected from orthopedic journals using the Dimensions App. Multilevel mixed-effects linear regression was used to address clustering in articles from the same journal, with journals as the leveling variable.</p><p><strong>Results: </strong>A total of 750 articles from 25 journals were included. In the final multivariable model, the funding source (none, industry, government, foundation, university, or multiple), findings (positive, negative, neutral, or not applicable), and the journal's impact factor were significant at <i>P</i><.05.</p><p><strong>Conclusion: </strong>Predictors of AAS are similar to predictors of traditional bibliometrics. Future studies need prospective dynamic data to further elucidate the AAS. [<i>Orthopedics</i>. 20XX;4X(X):XXX-XXX.].</p>","PeriodicalId":19631,"journal":{"name":"Orthopedics","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bibliometric Analysis of Predictors of Altmetric Attention Scores in Orthopedic Research: Investigating Online Visibility.\",\"authors\":\"Muhammad Talal Ibrahim, Hamza Imran, Muhammad Hamza Shuja, Haider Sheraz, Andrew Howard, Shahryar Noordin\",\"doi\":\"10.3928/01477447-20240809-03\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Altmetric Attention Score (AAS) captures online attention received by a research article in addition to traditional bibliometrics. We present a comprehensive bibliometric analysis of high AAS articles and identify predictors of AAS in orthopedics.</p><p><strong>Materials and methods: </strong>The top 30 articles with highest AAS were selected from orthopedic journals using the Dimensions App. Multilevel mixed-effects linear regression was used to address clustering in articles from the same journal, with journals as the leveling variable.</p><p><strong>Results: </strong>A total of 750 articles from 25 journals were included. In the final multivariable model, the funding source (none, industry, government, foundation, university, or multiple), findings (positive, negative, neutral, or not applicable), and the journal's impact factor were significant at <i>P</i><.05.</p><p><strong>Conclusion: </strong>Predictors of AAS are similar to predictors of traditional bibliometrics. Future studies need prospective dynamic data to further elucidate the AAS. [<i>Orthopedics</i>. 20XX;4X(X):XXX-XXX.].</p>\",\"PeriodicalId\":19631,\"journal\":{\"name\":\"Orthopedics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Orthopedics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3928/01477447-20240809-03\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Orthopedics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3928/01477447-20240809-03","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
Bibliometric Analysis of Predictors of Altmetric Attention Scores in Orthopedic Research: Investigating Online Visibility.
Background: Altmetric Attention Score (AAS) captures online attention received by a research article in addition to traditional bibliometrics. We present a comprehensive bibliometric analysis of high AAS articles and identify predictors of AAS in orthopedics.
Materials and methods: The top 30 articles with highest AAS were selected from orthopedic journals using the Dimensions App. Multilevel mixed-effects linear regression was used to address clustering in articles from the same journal, with journals as the leveling variable.
Results: A total of 750 articles from 25 journals were included. In the final multivariable model, the funding source (none, industry, government, foundation, university, or multiple), findings (positive, negative, neutral, or not applicable), and the journal's impact factor were significant at P<.05.
Conclusion: Predictors of AAS are similar to predictors of traditional bibliometrics. Future studies need prospective dynamic data to further elucidate the AAS. [Orthopedics. 20XX;4X(X):XXX-XXX.].
期刊介绍：
For over 40 years, Orthopedics, a bimonthly peer-reviewed journal, has been the preferred choice of orthopedic surgeons for clinically relevant information on all aspects of adult and pediatric orthopedic surgery and treatment. Edited by Robert D''Ambrosia, MD, Chairman of the Department of Orthopedics at the University of Colorado, Denver, and former President of the American Academy of Orthopaedic Surgeons, as well as an Editorial Board of over 100 international orthopedists, Orthopedics is the source to turn to for guidance in your practice.
The journal offers access to current articles, as well as several years of archived content. Highlights also include Blue Ribbon articles published full text in print and online, as well as Tips & Techniques posted with every issue.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
