Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
An Evolutive and Scientometric Research on Tissue Engineering Reviews
9
Zitationen
8
Autoren
2019
Jahr
Abstract
Number of publications has been widely used as a measure of research output, especially academic and university research. Number of publications in tissue engineering (TE) has increased year by year since early 1990s. However, after an exponential growth phase, recently publications increase at lower rates, suggesting a consolidation process in which reviews become a relevant and high-evidence document type. The aim of this study is to perform a scientometric evaluation of published literature reviews on TE to assess the status of scientific evolution and confirm the consolidation of TE as a research area. Published reviews on TE from 1991 to 2018 were retrieved from Web of Science core collection and this corpus of knowledge was analyzed by growth rate, research area, source title, and citation. Our results revealed that TE can be considered a consolidating area as it leaves the forefront stage of a gompertzian growth curve model. Original research/review ratio is lineally decreasing during the past decade. The emergence of reviews serves to confirm and refute hypothesis and build up a more reliable theoretical framework as well as a guide for future educational approaches. Distribution assessment of categories and journals indicates the multidisciplinary profile of this area focused on the design and development of new tissues. Biomedical sciences become relevant productors of reviews as they need to support TE innovations with high evidence leading to a safer and more efficient treatment of current injuries and diseases. Impact statement Scientometric analysis of published reviews about tissue engineering (TE) suggests that TE can be considered a consolidating area as it leaves the forefront stage of a gompertzian growth curve model. Biomedical sciences become relevant productors of reviews as they need to support TE innovations with high evidence leading to a safer and more efficient treatment of current injuries and diseases.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.339 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.211 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.614 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.478 Zit.