## 4 Discussion  
The objective of this study was to develop a dynamic FE model for pre-clinical analysis of knee contact mechanics and kinematics based on standardized loading conditions and to  
Figure 11: Peak values of the patellar shift during weight-bearing squat for several initial patellar positions (left to right: 6 mm baja to 6 mm alta) and patellar tendon stiffnesses (blue: 1000 N/mm, orange: 500 N/mm, and green: 300 N/mm). A positive patellar shift is given for relative patellar movements along the femoral mediolateral axis in lateral direction. Figure 10: Peak values of the patellar tilt during weight-bearing squat for several initial patellar positions (left to right: 6 mm baja to 6 mm alta) and patellar tendon stiffnesses (blue: 1000 N/mm, orange: 500 N/mm, and green: 300 N/mm). Patellar tilt is given as rotation around the patellar proximo-distal axis. Rotations of the patella to the medial side have a positive sign. analyze the effects of various changes of anatomic properties of a knee, such as the patellar tendon stiffness or the proximo-distal position of the patella. This model was intended to be used to analyze multifarious design variants during the implant design and pre-clinical testing process using a fast and straightforward workflow. Therefore, it should be fast to adapt and solve. The reduced complexity of the model was achieved by partially replacing tissues and bone geometries by boundary conditions. Therefore, the informative value of the results was limited to implant kinematics, loads, and stresses. Findings according to the specific behavior of soft tissue structures surrounding the knee were limited to the explicitly modeled structures, such as the patellar tendon. However, established pre-clinical testing methods are either based on nonstandardized data (e.g., [44; 45]) or neglect soft tissue structures and other patient-specific parameters (e.g., [18]). Therefore, the limitation that only the effects of some soft tissue structures can be analyzed turns into an advantage relative to other established standardized pre-clinical testing methods. Another limitation may arise by the fact that implant geometries were used, which were slightly different from the implants used to create the standardized loads [31]. However, [PERSON] et al. [46] compared the axial tibial loads from measurements and a numeric model with the standardized loads, and even though they used a different ultra-congruent cruciate sacrificing implant than [PERSON] et al. [31], there were no significant differences for the quasi-static load cycles of sitting down and rising from a chair. These load cycles are similar to the knee bend cycle, which was also used in our study. Therefore, it is assumed that the use of the implant components of Columbus(r) UC did not cause substantial differences. The remaining data from the given dataset, which were not used as input for the simulation, could be used to evaluate the simulation results. The moment around the flexion axis (\(M_{x}\)) and the varus-valgus moment (\(M_{y}\)) were in good accordance with deep flexion angles, and a deviation could be seen in extension. Regarding the moment around the tibial axis (\(M_{z}\)), the simulation results were also consistent with the dataset from literature for small flexion angles. As shown in Figure 5, all of the curves show characteristics which are similar to the standardized load data. Therefore, the model seems to be reliable according to tibial moments and forces, especially for deeper flexion angles, which are more relevant and critical to simulate. The contact forces in the tibial conodyles were compared to an analysis of [PERSON] et al. [47], who calculated the medial force ratios from nine patients for different daily activities. Some of their patients were also in the patient cohort for the standardized loads [31], which were used in the present study.