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Article| Volume 40, ISSUE 1, P71-81, January 2020

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Mapping research in assisted reproduction worldwide

Published:October 29, 2019DOI:https://doi.org/10.1016/j.rbmo.2019.10.013

      Abstract

      Research question

      What are the current research trends in human assisted reproduction around the world?

      Design

      An analysis of 26,000+ scientific publications (articles, letters and reviews) produced worldwide between 2005 and 2016. The corpus of publications indexed in PubMed was obtained by combining the Medical Subject Heading (MeSH) terms: ‘Reproductive techniques’, ‘Reproductive medicine’, ‘Reproductive health’, ‘Fertility’, ‘Infertility’ and ‘Germ cells’. An analysis was then carried out using text mining algorithms to obtain the main topics of interest.

      Results

      A total of 44 main topics were identified, which were then further grouped into 11 categories: ‘Laboratory techniques’, ‘Male factor’, ‘Quality of ART, ethics and law’, ‘Female factor’, ‘Public health and infectious diseases’, ‘Basic research and genetics’, ‘Pregnancy complications and risks’, ‘General – infertility & ART’, ‘Psychosocial aspects’, ‘Cancer’ and ‘Research methodology’. The USA was the leading country in terms of number of publications, followed by the UK, China and France. Research content in high-income countries is fairly homogeneous across categories and it is dominated by ‘Laboratory techniques’ in Western-Southern Europe, and by ‘Quality of ART, ethics and law’ in North America, Australia and New Zealand. ‘Laboratory techniques’ is also the most abundant category on a yearly basis.

      Conclusions

      This study identifies the current hot topics on human assisted reproduction worldwide and their temporal trends for 2005–2016. This provides an innovative picture of the current research that could help explore the areas where further research is needed.

      Keywords

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      References

        • Aleixandre-Benavent R.
        • Simon C.
        • Fauser B.C.
        Trends in clinical reproductive medicine research: 10 years of growth.
        Fertil. Steril. 2015; 104 (131–137 e135)
        • Arun R.
        • Suresh V.
        • Veni Madhavan C.E.
        • Narasimha Murthy M.N.
        On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations.
        in: Zaki M.J. Yu J.X. Ravindran B. Pudi V. Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, Berlin, Heidelberg2010: 391-402
        • Barratt C.L.R.
        • Bjorndahl L.
        • De Jonge C.J.
        • Lamb D.J.
        • Osorio Martini F.
        • McLachlan R.
        • Oates R.D.
        • van der Poel S.
        • St John B.
        • Sigman M.
        • et al.
        The diagnosis of male infertility: an analysis of the evidence to support the development of global WHO guidance-challenges and future research opportunities.
        Hum. Reprod. Update. 2017; 23: 660-680
        • Bird S.
        • Klein E.
        • Loper E.
        Natural language processing with Python: analysing text with the natural language toolkit.
        ‘O’Reilly Media, Inc.’, 2009
        • Bishop C.M.
        Pattern recognition and machine learning.
        springer, 2006
        • Blei D.M.
        • Ng A.Y.
        • Jordan M.I.
        Latent dirichlet allocation.
        Journal of machine Learning research. 2003; 3: 993-1022
        • Borg I.
        • Groenen P.
        Modern Multidimensional Scaling: Theory and Applications.
        Journal of Educational Measurement. 2006; 40: 277-280
        • Börner K.
        • Chen C.
        • Boyack K.W.
        Visualizing knowledge domains.
        Annual review of information science and technology. 2003; 37: 179-255
        • Cassi L.
        • Lahatte A.
        • Rafols I.
        • Sautier P.
        • De Turckheim E.
        Improving fitness: Mapping research priorities against societal needs on obesity.
        Journal of Informetrics. 2017; 11: 1095-1113
        • Ciarli TaR I.
        The Relation between Research Priorities and Societal Demands: The Case of Rice.
        Research Policy. 2019; 48: 949-967
        • Duffy J.
        • Bhattacharya S.
        • Herman M.
        • Mol B.
        • Vail A.
        • Wilkinson J.
        • Farquhar C.
        • Cochrane G.
        • Fertility G.
        Reducing research waste in benign gynaecology and fertility research.
        BJOG. 2017; 124: 366-369
        • Evers J.L.
        The wobbly evidence base of reproductive medicine.
        Reprod. Biomed. Online. 2013; 27: 742-746
        • Griffiths T.L.
        • Steyvers M.
        Finding scientific topics.
        Proceedings of the National Academy of Sciences. 2004; 101: 5228-5235
        • Hofmann T.
        Probabilistic latent semantic analysis.
        in: Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence. 1999: 289-296
        • Inhorn M.C.
        • Patrizio P.
        Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century.
        Hum. Reprod. Update. 2015; 21: 411-426
        • Lin C.-J.
        Projected Gradient Methods for Nonnegative Matrix Factorization.
        Neural Computation. 2007; 19: 2756-2779
      1. Mccallum A.K. MALLET: A Machine Learning for Language Toolkit. 2002.

        • Petit-Zeman S.
        • Firkins L.
        • Scadding J.W.
        The James Lind Alliance: tackling research mismatches.
        Lancet. 2010; 376: 667-669
      2. Ràfols I., and Yegros, A. Is research responding to health needs? Social Observatory of La Caixa Foundation (refereed online publication), March 2018. 2018.

        • Sarewitz D.
        • Pielke Jr, R.A.
        The neglected heart of science policy: reconciling supply of and demand for science.
        environmental science and policy. 2007; 10: 5-16
        • Sievert C.
        • Shirley K.
        LDAvis: A method for visualizing and interpreting topics.
        in: Proceedings of the workshop on interactive language learning, visualization, and interfaces. 2014: 63-70
        • Steyvers M.
        • Griffiths T.
        Probabilistic topic models.
        Handbook of latent semantic analysis. 2007; 427: 424-440
      3. United Nations Statistics Division. Standard country or area codes for statistical use (M49).https://unstats.un.org/unsd/methodology/m49/. 2017.

        • van Eck N.J.
        • Waltman L.
        VOS: A New Method for Visualizing Similarities Between Objects.
        in: Decker R. Lenz H.-J. Advances in Data Analysis: Proceedings of the 30th Annual Conference of the Gesellschaft für Klassifikation e.V., Freie Universität Berlin, March 8–10, 2006, Berlin, Heidelberg Springer Berlin Heidelberg, 2007: 299-306
      4. World Bank Data 2017. World Bank Country and Lending Groups 2018.

        • Zegers-Hochschild F.
        • Adamson G.D.
        • Dyer S.
        • Racowsky C.
        • de Mouzon J.
        • Sokol R.
        • Rienzi L.
        • Sunde A.
        • Schmidt L.
        • Cooke I.D.
        • Simpson J.L.
        • van der Poel S.
        • et al.
        The International Glossary on Infertility and Fertility Care, 2017.
        Hum. Reprod. 2017; 32: 1786-1801

      Biography

      Dr Désirée García obtained her PhD at Barcelona University in 2017 and her BSc in Pharmacy in 2005. She also has a Master's Degree in Research Methodology and Statistics. In 2009, she joined Clínica EUGIN in Barcelona, where her current research focuses on reproductive medicine.
      Key Message
      This article identifies the current hot topics in human assisted reproduction worldwide and the temporal trends for the period 2005–2016. It provides an innovative picture of the current research that could help to explore the areas where further research is needed.