IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v16y2022i2s1751157722000141.html
   My bibliography  Save this article

Distinguishing transformative from incremental clinical evidence: A classifier of clinical research using textual features from abstracts and citing sentences

Author

Listed:
  • Shi, Xuanyu
  • Du, Jian

Abstract

In clinical research and clinical decision-making, it is important to know if a study changes or only supports the current standards of care for specific disease management. We define such a change as transformative and a support as incremental research. It usually requires a huge amount of domain expertise and time for humans to finish such tasks. Faculty Opinions provides us with a well-annotated corpus on whether a research challenges or only confirms established research. In this study, a machine learning approach is proposed to distinguishing transformative from incremental clinical evidence. The texts from both abstract and a 2-year window of citing sentences are collected for a training set of clinical studies recommended and labeled by Faculty Opinions experts. We achieve the best performance with an average AUC of 0.755 (0.705–0.875) using Random Forest as the classifier and citing sentences as the feature. The results showed that transformative research has more typical language patterns in citing sentences than abstract sentences. We provide an efficient tool for identifying those clinical evidence challenging or only confirming established claims for clinicians and researchers.

Suggested Citation

  • Shi, Xuanyu & Du, Jian, 2022. "Distinguishing transformative from incremental clinical evidence: A classifier of clinical research using textual features from abstracts and citing sentences," Journal of Informetrics, Elsevier, vol. 16(2).
    • Handle: RePEc:eee:infome:v:16:y:2022:i:2:s1751157722000141
    DOI: 10.1016/j.joi.2022.101262
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157722000141
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2022.101262?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    2. Du, Jian & Li, Peixin & Haunschild, Robin & Sun, Yinan & Tang, Xiaoli, 2020. "Paper-patent citation linkages as early signs for predicting delayed recognized knowledge: Macro and micro evidence," Journal of Informetrics, Elsevier, vol. 14(2).
    3. Hilda Bastian & Paul Glasziou & Iain Chalmers, 2010. "Seventy-Five Trials and Eleven Systematic Reviews a Day: How Will We Ever Keep Up?," PLOS Medicine, Public Library of Science, vol. 7(9), pages 1-6, September.
    4. Peiling Wang & Joshua Williams & Nan Zhang & Qiang Wu, 2020. "F1000Prime recommended articles and their citations: an exploratory study of four journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 933-955, February.
    5. Ludo Waltman & Rodrigo Costas, 2014. "F1000 Recommendations as a Potential New Data Source for Research Evaluation: A Comparison With Citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(3), pages 433-445, March.
    6. Marc Bertin & Iana Atanassova & Cassidy R. Sugimoto & Vincent Lariviere, 2016. "The linguistic patterns and rhetorical structure of citation context: an approach using n-grams," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1417-1434, December.
    7. Jian Du & Xiaoli Tang & Yishan Wu, 2016. "The effects of research level and article type on the differences between citation metrics and F1000 recommendations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(12), pages 3008-3021, December.
    8. Small, Henry & Tseng, Hung & Patek, Mike, 2017. "Discovering discoveries: Identifying biomedical discoveries using citation contexts," Journal of Informetrics, Elsevier, vol. 11(1), pages 46-62.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lutz Bornmann & Robin Haunschild & Sven E. Hug, 2018. "Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 427-437, February.
    2. Lutz Bornmann & K. Brad Wray & Robin Haunschild, 2020. "Citation concept analysis (CCA): a new form of citation analysis revealing the usefulness of concepts for other researchers illustrated by exemplary case studies including classic books by Thomas S. K," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 1051-1074, February.
    3. Tahamtan, Iman & Bornmann, Lutz, 2018. "Core elements in the process of citing publications: Conceptual overview of the literature," Journal of Informetrics, Elsevier, vol. 12(1), pages 203-216.
    4. Lutz Bornmann & Alexander Tekles, 2019. "Disruptive papers published in Scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 331-336, July.
    5. Small, Henry, 2018. "Characterizing highly cited method and non-method papers using citation contexts: The role of uncertainty," Journal of Informetrics, Elsevier, vol. 12(2), pages 461-480.
    6. Yuyan Jiang & Xueli Liu, 2023. "A construction and empirical research of the journal disruption index based on open citation data," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(7), pages 3935-3958, July.
    7. Min, Chao & Bu, Yi & Sun, Jianjun, 2021. "Predicting scientific breakthroughs based on knowledge structure variations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    8. Bornmann, Lutz & Tekles, Alexander & Zhang, Helena H. & Ye, Fred Y., 2019. "Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data," Journal of Informetrics, Elsevier, vol. 13(4).
    9. Wang, Peiling & Su, Jing, 2021. "Post-publication expert recommendations in faculty opinions (F1000Prime): Recommended articles and citations," Journal of Informetrics, Elsevier, vol. 15(3).
    10. Iman Tahamtan & Lutz Bornmann, 2019. "What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1635-1684, December.
    11. Hoekman, Jarno & Rake, Bastian, 2024. "Geography of authorship: How geography shapes authorship attribution in big team science," Research Policy, Elsevier, vol. 53(2).
    12. Xian Li & Ronald Rousseau & Liming Liang & Fangjie Xi & Yushuang Lü & Yifan Yuan & Xiaojun Hu, 2022. "Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2105-2122, April.
    13. Martin Ricker, 2017. "Letter to the Editor: About the quality and impact of scientific articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1851-1855, June.
    14. Jianhua Hou & Xiucai Yang & Yang Zhang, 2023. "The effect of social media knowledge cascade: an analysis of scientific papers diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5169-5195, September.
    15. Michele Pezzoni & Fabiana Visentin, 2024. "Gender bias in team formation: the case of the European Science Foundation’s grants," Science and Public Policy, Oxford University Press, vol. 51(2), pages 247-260.
    16. Cinzia Daraio & Simone Di Leo & Loet Leydesdorff, 2022. "Using the Leiden Rankings as a Heuristics: Evidence from Italian universities in the European landscape," LEM Papers Series 2022/08, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    17. Li, Heyang & Wu, Meijun & Wang, Yougui & Zeng, An, 2022. "Bibliographic coupling networks reveal the advantage of diversification in scientific projects," Journal of Informetrics, Elsevier, vol. 16(3).
    18. K. Brad Wray & Søren R. Paludan & Lutz Bornmann & Robin Haunschild, 2024. "Using Reference Publication Year Spectroscopy (RPYS) to analyze the research and publication culture in immunology," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3271-3283, June.
    19. Mingyang Wang & Zhenyu Wang & Guangsheng Chen, 2019. "Which can better predict the future success of articles? Bibliometric indices or alternative metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1575-1595, June.
    20. Pan Zhang & Yongjun Du & Sijie Han & Qingan Qiu, 2022. "Global Progress in Oil and Gas Well Research Using Bibliometric Analysis Based on VOSviewer and CiteSpace," Energies, MDPI, vol. 15(15), pages 1-27, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:16:y:2022:i:2:s1751157722000141. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.