Poster Presentation 50th International Society for the Study of the Lumbar Spine Annual Meeting 2024

The Structure of Spine Biomechanics Research (#16)

Jeff Barrett 1 , Stephen HM Brown 2 , Thomas R Oxland 1 , Nima Ashjaee 3
  1. The University of British Columbia, Vancouver, BRITISH COLUMBIA, Canada
  2. Department of Human Health & Nutritional Sciences, University of Guelph, Guelph, Ontario, Canada
  3. International Collaboration on Repair Discoveries (ICORD), Vancouver, British Columbia, Canada

Introduction: In his 1962 book "The Structure of Scientific Revolutions," Thomas Kuhn elucidates the five phases in the evolution of a scientific field1. Phase 1, the pre-paradigm stage, represents a period where no consensus exists among the community regarding its central tenets. Phase 2, termed "Normal Science," materializes when a core theory becomes the linchpin of the discipline, guiding routine problem-solving efforts. Gradually, during this phase, anomalies accumulate as experimental results deviate from theory. This mounting discordance initiates phase 3, crisis, eventually leading to a transformative new paradigm in phase 4. After the establishment of the new paradigm, the field returns to Normal Science in phase 5.

Kuhn illustrates this cyclical structure through historical examples and posits that innovations tend to emerge at the boundaries between disciplines. He suggests that an analysis of citation structures within a discipline can offer insights into its organization and interface points. Consequently, this study aims to construct a co-citation network2 for the examination of citation patterns in spine biomechanics. Our objectives include identifying key themes within this discipline and uncovering the types of research questions that exist at the interfaces between its subtopics.

Methods: We gathered data from the Web of Science Core Collection using the search query "spine AND (mech* OR biomech*)," totaling 41,472 records as of July 24, 2023. We created a co-citation network, with the metaknowledge3 Python library, that we visualized in Gephi4, where we filtered out the giant component and papers with at least 3 co-citations, totalling 8,958 papers. Applying a community detection algorithm5, we classified the network into 20 categories, with just 9 covering over 90% of the papers. By analyzing common words in each category, and consulting experts for topic confirmation, we identified key themes.

Results: The network analysis identified nine large communities within the spine biomechanics discipline, with a considerable overlap between them (Figure 1).

6553b34ea9f5f-2023-11-03-ISSLS-figure-resized.png

Discussion: The network structure in spine biomechanics forms a star-shaped pattern, with a central community dedicated to quantifying lumbar spine mechanical properties and validating spine models. Eight sub-topics radiate from this central hub, maintaining strong links with it but fewer connections among themselves. This setup suggests that the mechanical properties of the spine, encompassing bones and supporting tissues, act as a central paradigm that guides research in spine biomechanics. Four communities focus on fixation techniques and vertebral augmentation, emphasizing the field's commitment to addressing the challenges of spine surgery, directly impacting advancements in surgical techniques and patient care.

While the network structure indicates a state of "normal science" in spine biomechanics, it's essential to consider unexplored aspects. External disciplines like artificial intelligence and machine learning have the potential to revolutionize science broadly, including spine biomechanics, by introducing innovative methods and approaches to research. Recognizing the potential for cross-disciplinary knowledge exchange underscores the dynamic and evolving nature of scientific research, hinting at possible paradigm shifts on the horizon.

 

 

 

  1. Kuhn, T. S. The Structure of Scientific Revolutions. (University of Chicago Press, 1962).
  2. McAllister, J. T., Lennertz, L. & Atencio Mojica, Z. Mapping A Discipline: A Guide to Using VOSviewer for Bibliometric and Visual Analysis. Sci. Technol. Libr. 41, 319–348 (2022).
  3. McLevey, J. & McIlroy-Young, R. Introducing metaknowledge : Software for computational research in information science, network analysis, and science of science. J. Informetr. 11, 176–197 (2017).
  4. Bastian, M., Heymann, S. & Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. Proc. Int. AAAI Conf. Web Soc. Media 3, 361–362 (2009).
  5. Lambiotte, R., Delvenne, J.-C. & Barahona, M. Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks. IEEE Trans. Netw. Sci. Eng. 1, 76–90 (2014).