Introduction.- The model presented in part I (19 predictors) had good predictive values for non-return to work 2 years after vocational rehabilitation for orthopaedic trauma. However, the number of predictors is high for the detection of patients at risk in a clinic. For example, the INTERMED for itself consists of 20 questions and needs 20 minutes to be filled in. For this reason, the aim of this study was to compare the predictive value of different models for the prediction of non-return to work.Patients and methods.- In this longitudinal prospective study, the cohort consisted of 2156 included inpatients with orthopaedic trauma attending a rehabilitation hospital after a work, traffic, sport or leisure related injury. Two years after discharge, 1502 patients returned a questionnaire regarding return to work. We compared the area under the receiver-operator-characteristics curve (ROC) between different models: INTERMED total score, the 4 partial INTERMED scores, the items of the most predictive partial score; with or without confounders.Results.- The ROC for the total score of the INTERMED plus the five confounders of the of the part one (qualified work, speaking French, lesion of upper extremity, education and age) was 0.72. The sole partial INTERMED score to predict return to work was the social sub score. The ROC for the five items of the latter sub score of the INTERMED was 0.69. The ROC for the five items of the social subscale of the INTERMED combined with five predictors was 0.73. This was significantly better than the use of only the five items from INTERMED alone (delta 0.034; 95% CI 0.017 to .050). The model presented in part I (INTERMED total score plus 18 predictors) was not significantly better than the five items INTERMED social score plus five confounders.Discussion.- The use of a model with ten variables (INTERMED social five items plus five confounders) has good predictive value to detect patients not returning to work after vocational rehabilitation after orthopaedic trauma. The parsimony of this model facilitates its use in a clinic for the detection of patients at risk.