title: Descriptive models of restorative treatment decisions.
abstract: OBJECTIVE
  This study developed descriptive models of dentists' restorative treatment decisions for individual teeth. Such models could be useful in personnel planning, in assessing the effects of dental treatment programs, and in furthering understanding of dentists' decision-making processes.
  
  
  METHODS
  Logistic regression was used to construct models of the probability of individual teeth receiving a recommendation for restorative treatment. Independent variables for the models were data from epidemiologic oral examinations and self-administered questionnaires of subjects who were seeking treatment at a dental school. Data for the dependent variable, the probability of treatment, were collected from multiple dentists' treatment plans of these subjects. Separate models were constructed for molar, premolar, and anterior teeth. An assessment of the models' utilities in a different population consisted of comparing the treatment probabilities estimated by the models with those actually experienced by a community sample of 317 individuals who visited dentists in the 18 months following our examination.
  
  
  RESULTS
  Constructed models for molar, premolar, and anterior teeth returned kappa values of 0.60, 0.62, and 0.65, respectively, for the original data set. The models were less accurate in identifying which teeth received treatment among subjects in the community sample, with kappas of 0.10, 0.18, and 0.20, respectively.
  
  
  CONCLUSIONS
  Models of dentists' restorative treatment decision making based on clinical and nonclinical data can determine the probability of treatment for individual teeth with reasonable accuracy. Hence, the approach holds promise for developing measures of normative treatment need. However, the models are not accurate predictors of dichotomous decisions by individual dentists regarding treatment interventions. Both differences in the subject samples used to develop and assess the models and individual dentist idiosyncrasies may contribute to this inaccuracy.
authors: Sheps Center for Health Services Research, University of North Carolina, Chapel Hill 27599-7590, USA. jim_bader@unc.edu
authors: J D
authors: JD
authors: Bader
authors: D A
authors: DA
authors: Shugars
mesh_terms: D000328:Adult; D001641:Bicuspid; D003481:Cuspid; D003657:Decision Making; D003661:Decision Support Techniques; D003729:Dental Care; D003793:Dental Restoration, Permanent; D003815:Dentists; D004812:Epidemiologic Methods; D005260:Female; D006301:Health Services Needs and Demand; D006801:Humans; D007180:Incisor; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008963:Molar; D010347:Patient Care Planning; D011336:Probability; D011795:Surveys and Questionnaires