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Bibliometric and Visualized Analysis of Research Relating to Spinal Modic Changes Reported Over the Period 2004-2024.

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Summary
This summary is machine-generated.

This bibliometric analysis of Modic changes (MC) reveals research trends and collaboration patterns. Future studies should focus on MC classification, mechanisms, and the impact of obesity and muscle strength.

Keywords:
CiteSpaceModic changesbibliometricsclinical relevancevisual analysis

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Area of Science:

  • Spinal imaging and diagnostics
  • Musculoskeletal research
  • Bibliometrics and research evaluation

Background:

  • Modic changes (MC) are vertebral endplate alterations linked to degenerative disc disease and low back pain.
  • Existing research on MC is fragmented, lacking comprehensive quantitative analysis of trends and collaborations.
  • Understanding research patterns is crucial for advancing the field of spinal MC.

Purpose of the Study:

  • To conduct the first bibliometric analysis and visualization of research on spinal Modic changes (MC).
  • To evaluate global research landscape, identify hotspots, and analyze collaboration networks in the field of spinal MC.

Main Methods:

  • Retrieved 603 publications on spinal MC from Web of Science Core Collection (2004-2024).
  • Utilized CiteSpace software for bibliometric analysis of keywords, authors, institutions, countries, and cited literature.
  • Analyzed publication volume, impact, and collaboration patterns.

Main Results:

  • China led in publication volume; University of Oulu had the most significant institutional impact.
  • J. Karppinen was the most prolific author; M. Modic was the most cited.
  • Key research hotspots included 'degenerative disease,' 'bone marrow changes,' 'obesity,' and 'paraspinal muscles.'

Conclusions:

  • This study provides a comprehensive overview of spinal MC research, highlighting key areas and collaborations.
  • Future research should focus on MC classification, pathophysiology, clinical significance, and the role of obesity and muscle.
  • Investigating AI applications for MC identification and understanding is recommended.