Eigenfactor score and alternative bibliometrics surpass the impact factor in a 2-years ahead annual-citation calculation: a linear mixed design model analysis of Radiology, Nuclear Medicine and Medical Imaging journals.

INTRODUCTION: Because we believe the journal selection before a manuscript submission deserves further investigation in each medical specialty, we aimed to evaluate the predictive ability of seven bibliometrics in the Radiology, Nuclear Medicine and Medical Imaging category of the Web of Knowledge to calculate total citations over a 7-year period. METHODS: A linear mixed effects design using random slopes and intercepts were performed on bibliometrics corresponding to 124 journals from 2007 to 2011, with their corresponding citations from 2009 to 2013, which appeared in the Journal Citations Report Science Edition. RESULTS: The Eigenfactor Score, Article Influence Score, Cited Half-life, 5-years impact factor and Number of Articles are significant predictors of 2-year-ahead total citations (p ≤ 0.010 for all variables). The impact factor and Immediacy Index are not significant predictors. There was a significant global effect size (R2 = 0.934; p < 0.001), which yielded a total variance of 93.4%. CONCLUSIONS: Our findings support researchers' decision to stop the misuse of IF alone to evaluate journals. Radiologists and other researchers should review journal's bibliometrics for their decision-making during the manuscript submission phase. A re-ranking of journals using Eigenfactor Score, Article Influence Score, and Cited Half-life provides a better assessment of their significance and importance in particular disciplines.

Alternative bibliometrics from the web of knowledge surpasses the impact factor in a 2-year ahead annual citation calculation: Linear mixed-design models' analysis of neuroscience journals.

CONTEXT: The decision about which journal to choose for the publication of research deserves further investigation. AIMS: In this study, we evaluate the predictive ability of seven bibliometrics in the Web of Knowledge to calculate total cites over a 7-year period in neuroscience journals. SETTINGS AND DESIGN: Coincidental bibliometrics appearing during 2007, 2008, 2009, 2010, and 2011, along with their corresponding cites in 2009, 2010, 2011, 2012, and 2013, were recorded from the journal citation reports (JCR) Science Edition. This was a retrospective study. MATERIALS AND METHODS: This was a bibliographic research using data from the Web of Knowledge in the neuroscience category. STATISTICAL ANALYSIS USED: A linear-mixed effects design using random slopes and intercepts was performed on 275 journals in the neuroscience category. RESULTS: We found that Eigenfactor score, cited half-life, immediacy index, and number of articles are significant predictors of 2-year-ahead total cites (P ≤ 0.010 for all variables). The impact factor, 5-year impact factor, and article influence score were not significant predictors; the global effect size was significant (R2= 0.999; P < 0.001) with a total variance of 99.9%. CONCLUSIONS: An integrative model using a set of several metrics could represent a new standard to assess the influence and importance of scientific journals, and may simultaneously help researchers to rank journals in their decision-making during the manuscript submission phase.