Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 30, 2020
Date Accepted: Jul 5, 2021
Date Submitted to PubMed: Nov 29, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Information and scientific impact of Advanced Therapies in the age of mass media: an Altmetrics-based analysis of Tissue Engineering.
ABSTRACT
Background:
Tissue engineering (TE) constitutes a multidisciplinary field aiming to construct artificial tissues to regenerate end-staged organs. Its development has taken placed since the last decade of the 20th century, entailing a clinical revolution. In this sense, TE research groups have worked and shared relevant information in the mass media era. Thus, it would be interesting to study the online dimension of TE research and to compare it with traditional measures of scientific impact.
Objective:
To evaluate TE online dimension from 2012 to 2018 by using metadata obtained from the Web of Science (WoS) and Altmetrics and to develop a prediction equation for the impact of TE documents from Almetrics scores.
Methods:
We have analyzed 23,719 TE documents through descriptive and statistical methods. First, TE temporal evolution was exposed for WoS and fifteen online platforms (News, Blogs, Policy, Twitter, Patents, Peer review, Weibo, Facebook, Wikipedia, Google, Reddit, F1000, Q&A, Video and Mendeley readers). The 10 most-cited TE original articles were ranked according to WoS citations and the Altmetric Attention Score. Second, in order to better comprehend TE online framework, a correlation and factorial analysis were performed based on the suitable results previously obtained for the Bartlett Sphericity and Kaiser-Meyer-Olkin tests. Finally, the liner regression model was applied to elucidate the relation between academy and online media and to construct a prediction equation for TE from Altmetrics data.
Results:
TE dynamic shows an upward trend in WoS citations, Twitter, Mendeley Readers and Altmetric Scores. However, WoS and Altmetrics rankings for the most cited documents clearly differs. When compared, the best correlation results were obtained for Mendeley readers and WoS (Ï=0.71). In addition, the factorial analysis identified six factors that could explain the previously observed differences between TE academy, and the online platforms evaluated. At this point, the mathematical model constructed is able to predict and explain more than the 40% of TE WoS citations from Altmetrics scores.
Conclusions:
The scientific information related to the construction of bioartificial tissues increasingly reaches society through different online media. Because of the focus of TE research importantly differs when the academic institutions and online platforms are compared, it could be stated that basic and clinical research groups, academic institutions and health politicians should take it into account in a coordinated effort oriented to the design and implementation of adequate strategies for information diffusion and population health education.
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