title: Factors affecting the clinical approach to impacted maxillary canines: A Bayesian network analysis.
abstract: BACKGROUND
  The aim of this study was to apply Bayesian networks to evaluate the relative role and possible causal relationships among various factors affecting the diagnosis and final treatment outcome of impacted maxillary canines.
  
  
  METHODS
  A total of 168 patients with infraosseous impacted maxillary canines had a combined surgical-orthodontic approach aimed to guide the impacted tooth to the center of the alveolar ridge. Demographic, orthodontic, and periodontal variables were recorded and analyzed by means of Bayesian network analysis.
  
  
  RESULTS
  All 168 impacted canines were successfully moved and aligned in the dental arches with healthy periodontiums. According to the Bayesian network analysis, bilateral impaction was associated with palatal impaction and longer treatment; the pretreatment alpha-angle was a determinant for the duration of orthodontic traction, also because of the associations between greater angulation of impacted canines with more severe tooth displacement and with greater distance of the impacted canine from the occlusal plane; the posttreatment periodontal outcome was not related to the pretreatment radiographic variables.
  
  
  CONCLUSIONS
  Bayesian network analysis was useful to identify possible relationships among the variables considered for diagnosis and treatment of impacted canines.
authors.affiliation: Department of Periodontology, University of Florence, Italy.
authors.forename: Michele
authors.initials: M
authors.lastname: Nieri
authors.forename: Aldo
authors.initials: A
authors.lastname: Crescini
authors.forename: Roberto
authors.initials: R
authors.lastname: Rotundo
authors.forename: Tiziano
authors.initials: T
authors.lastname: Baccetti
authors.forename: Pierpaolo
authors.initials: P
authors.lastname: Cortellini
authors.forename: Giovan Paolo
authors.initials: GP
authors.lastname: Pini Prato
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000465:Algorithms; D001499:Bayes Theorem; D002648:Child; D003481:Cuspid / Q000473:pathology*; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008437:Maxilla; D008875:Middle Aged; D016571:Neural Networks, Computer; D050793:Orthodontic Extrusion / Q000295:instrumentation / Q000379:methods*; D010519:Periodontium / Q000033:anatomy & histology; D011859:Radiography; D012737:Sex Factors; D014095:Tooth, Impacted / Q000000981:diagnostic imaging / Q000473:pathology* / Q000601:surgery / Q000628:therapy*; D016896:Treatment Outcome; D055815:Young Adult