text: For the first analysis, binary logistic regression was used 
  to determine predictors of any healthcare utilization fol- 
  lowing physical 
  therapy, with the dependent variable 
  defined as reporting one or more utilization events for any 
  the entire 
  of 
  follow-up period. For analyses of 
  specific services, 
  utilization was dichotomized for each service. Specific 
  service utilization over early (through 6 months) and late 
  (6 months to 12 months) phases following physical ther- 
  apy were collapsed to create a single dichotomous 
  utilization indicator for each service over the entire study 
  follow-up period. Any utilization of the service over that 
  period was categorized as YES. Separate multivariate bin- 
  ary logistic regression models were then fitted for the 
  dichotomous utilization indicator (i.e. YES or NO) of each 
  healthcare service (e.g. opioid use, injection, imaging, sur- 
  gery, and emergency room visits). 
  
  the potential healthcare services over 
  
  For all analyses, full hierarchical multivariate models 
  were first fit to assess the unique contributions of each 
  block. This approach allowed us to determine the rela- 
  tive 
  and 
  health-related 
  developed 
  OSPRO-ROS and OSPRO-YF tools, and response to 
  treatment via time varying variables (e.g., pain intensity 
  and region specific function). However, since our pri- 
  mary 
  and accurate 
  utilization prediction models for efficient assessment of 
  risk, we then separately developed stepwise models using 
  backward selection for each dependent variable to derive 
  parsimonious prediction item sets. Parsimonious models 
  were chosen as a more conservative approach to identi- 
  fying individual predictors given the potential for overfit- 
  ting full multivariate models because of high subject 
  attrition. For stepwise models, the p-value threshold was 
  0.05 for entry and 0.10 for removal. Overall fit for each 
  model was examined with Hosmer & Lemeshow test, 
  chi-square and pseudo-R2 values (e.g. Nagelkerke) when 
  
  contributions 
  
  of 
  variables,  baseline 
  the  demographic 
  newly 