Introduction
Tissue Engineering (TE) is a multidisciplinary discipline aiming to
develop biological substitutes that can restore, maintain, or even
improve the structure or functionality of damaged tissues (Langer &
Vacanti, 1993). Since its appearance in 1988 (Viola, Bal, & Grad,
2003), TE has globally spread to improve current therapeutic approaches
entailing a revolution in health sciences (Kaul & Ventikos, 2015). In
this sense, several TE devices have been employed in the treatment of
damaged blood vessels (Kumar, Brewster, Caves, & Chaikof, 2011),
peripheral nerve injuries (Carriel, Alaminos, Garzon, Campos, &
Cornelissen, 2014), chronic skin ulcerations (Debels, Hamdi, Abberton,
& Morrison, 2015) , oral mucosal replacement (Martín-Piedra et al.,
2016; Sanchez-Quevedo et al., 2007) and corneal lesions (Rico-Sánchez et
al., 2019).
This growing interest in TE research has been demonstrated through the
increasing number of TE documents, such as the recent literature states
(Santisteban-Espejo et al., 2018). Besides, in order to identify TE
global trends and to define the cognitive and social framework that
undergoes TE scientific evolution, several analyses have also been
performed (Santisteban-Espejo et al., 2019). These bibliometric-based
studies can help administrative authorities to better plan funding
allocations and to promote synergies within TE scientific community, as
previously stated in other scientific areas (Abramo, D’Angelo, &
Caprasecca, 2009).
In this context, traditional bibliometric analysis has used the
information extracted from academic documents (i.e. citations or
keywords) in order to understand the development of TE area (Dai G,
2000; Santisteban-Espejo et al., 2019; Santisteban-Espejo et al., 2018).
Nevertheless, these classical bibliometric methods have been criticized
in recent years because their fewer adequacy to comprehend the real
online attention of scientific research. As a consequence, alternative
metrics, formerly called Altmetrics, have been developed to evaluate
scientific behaviour through information content at social media (Priem,
Piwowar, & Hemminger, 2011).
This altmetric methodology describes a web-based metric for the impact
of publications and other scholarly material by using data from social
media platforms (i.e.Twitter, Facebook, Google+, blogs, Mendeley,
CiteULike, Reddit and Wikipedia, among others) (Veeranjaneyulu, 2017).
The appearance of this type of measures is related to the social media
revolution; there are now different groups of the population, non-author
professionals, which read research articles and now also share them and,
furthermore, new types of academic outputs have appeared (Moral-Munoz &
Cobo, 2018).
Consequently, the traditional acceptance that the research output only
was disseminated within the scientific community has now changed. In
addition, the online public nature of these alternative tools permits to
track mentions of scholarly articles across the online landscape faster
and broader than traditional citation metrics (Verma, 2018). The impact
of the Altmetrics and its complementary role in association with
traditional bibliometrics have been well stated in several disciplines
(Haustein, Bowman, & Costas, 2016).
In this context of global science, where information is shared in the
social web, even previously to its communication within the academic
community, it would be interesting to analyse the real scientific impact
of TE in our society. To our knowledge, no documents are currently
available evaluating this aspect in TE research field. Therefore, the
primary aim of this study is to determine the online dimension of TE
scientific production in social media and to correlate it with
traditional scholarly impact. Then, we perform a factorial analysis to
identify the components that could explain the correlation results and,
finally, we developed a prediction equation for future TE citations
based on altmetrics data.