title: Machine Learning-Based Classification of 38 Years of Spine-Related Literature Into 100 Research Topics.
abstract: METHODS
  Retrospective review.
  
  
  OBJECTIVE
  To identify the top 100 spine research topics.
  
  
  BACKGROUND
  Recent advances in "machine learning," or computers learning without explicit instructions, have yielded broad technological advances. Topic modeling algorithms can be applied to large volumes of text to discover quantifiable themes and trends.
  
  
  METHODS
  Abstracts were extracted from the National Library of Medicine PubMed database from five prominent peer-reviewed spine journals (European Spine Journal [ESJ], The Spine Journal [SpineJ], Spine, Journal of Spinal Disorders and Techniques [JSDT], Journal of Neurosurgery: Spine [JNS]). Each abstract was entered into a latent Dirichlet allocation model specified to discover 100 topics, resulting in each abstract being assigned a probability of belonging in a topic. Topics were named using the five most frequently appearing terms within that topic. Significance of increasing ("hot") or decreasing ("cold") topic popularity over time was evaluated with simple linear regression.
  
  
  RESULTS
  From 1978 to 2015, 25,805 spine-related research articles were extracted and classified into 100 topics. Top two most published topics included "clinical, surgeons, guidelines, information, care" (n = 496 articles) and "pain, back, low, treatment, chronic" (424). Top two hot trends included "disc, cervical, replacement, level, arthroplasty" (+0.05%/yr, P < 0.001), and "minimally, invasive, approach, technique" (+0.05%/yr, P < 0.001). By journal, the most published topics were ESJ-"operative, surgery, postoperative, underwent, preoperative"; SpineJ-"clinical, surgeons, guidelines, information, care"; Spine-"pain, back, low, treatment, chronic"; JNS- "tumor, lesions, rare, present, diagnosis"; JSDT-"cervical, anterior, plate, fusion, ACDF."
  
  
  CONCLUSIONS
  Topics discovered through latent Dirichlet allocation modeling represent unbiased meaningful themes relevant to spine care. Topic dynamics can provide historical context and direction for future research for aspiring investigators and trainees interested in spine careers. Please explore https://singdc.shinyapps.io/spinetopics.
  
  
  METHODS
  N A.
authors.affiliation: Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA.
authors.forename: David C
authors.initials: DC
authors.lastname: Sing
authors.forename: Lionel N
authors.initials: LN
authors.lastname: Metz
authors.forename: Stefan
authors.initials: S
authors.lastname: Dudli
mesh_terms: D006801:Humans; D000069550:Machine Learning; D009985:Orthopedics / Q000145:classification / Q000639:trends*; D011642:Publications / Q000145:classification / Q000639:trends*; D012106:Research / Q000145:classification / Q000639:trends*