Objective: To examine the biomechanical stress distribution at the upper instrumented vertebra (UIV) according to unicortical-and bicortical purchase model by finite element analysis (FEA). Methods: A T8 to Sacrum with implant finite element model was developed and validated. The pedicle screws were unicortically or bicortically inserted from T10 to L5, and each model was compared and the von Mises (VM) yield stress of T10 was calculated. According to the motion (flexion, extension, lateral bending, and axial rotation) of spine, boundary condition values were set as 15°, 15°, 10°, 4°. Results: Although the 2 stress values did not show a significant difference between the unicortical-and bicortical purchase models in the flexion and extension, bicortical purchase model showed a larger stress distribution. However, the asymmetric behavior was significantly greater in the case of lateral bending (0.802 MPa vs. 0.489 MPa) and the rotation (5.545 MPa vs. 4.905 MPa). The greater stress was observed on the spinal body surface abutting the implanted screw. Although the maximum stress was observed around the implanted screw in the bicortical purchase model under axial loading, the VM stress of both models was not significantly different. Conclusion: Bicortical purchase model showed a larger stress distribution than the unicortical model, especially in the case of lateral bending and the rotation behavior. Our biomechanical simulation by FEA indicates that bicortical fixation at UIV can be a risk factor for early UIV compression fracture after adult spinal deformity surgery.
Considering the high variability of the S, it is better to direct the IS screw trajectory toward the opposite upper corner of the S at the level of first sacral foramen. If a TITS screw is needed, the transverse fixation for the S could be performed alternatively due to its sufficient osseous site even in Asian sacrum.
Background: Arm swing plays a role in gait by accommodating forward movement through trunk balance. This study evaluates the biomechanical characteristics of arm swing during gait.Methods: The study performed computational musculoskeletal modeling based on motion tracking in 15 participants without musculoskeletal or gait disorder. A three-dimensional (3D) motion tracking system using three Azure Kinect (Microsoft) modules was used to obtain information in the 3D location of shoulder and elbow joints. Computational modeling using AnyBody Modeling System was performed to calculate the joint moment and range of motion (ROM) during arm swing.Results: The mean ROM of the dominant elbow was 29.7°±10.2° and 14.2°±3.2° in flexion–extension and pronation–supination, respectively. The mean joint moment of the dominant elbow was 56.4±12.7 Nm, 25.6±5.2 Nm, and 19.8±4.6 Nm in flexion–extension, rotation, and abduction–adduction, respectively. Conclusions: The elbow bears the load created by gravity and muscle contracture in dynamic arm swing movement.
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