ABSTRACT:The relationships between non-contact anterior cruciate ligament injuries and the underlying biomechanics are still unclear, despite large quantities of academic research. The purpose of this research was to study anterior cruciate ligament strain during jump landing by investigating its correlation with sagittal plane kinetic/kinematic parameters and by creating an empirical model to estimate the maximum strain. Whole-body kinematics and ground reaction forces were measured from seven subjects performing single leg jump landing and were used to drive a musculoskeletal model that estimated lower limb muscle forces. These muscle forces and kinematics were then applied on five instrumented cadaver knees using a dynamic knee simulator system. Correlation analysis revealed that higher ground reaction force, lower hip flexion angle and higher hip extension moment among others were correlated with higher peak strain (p < 0.05). Multivariate regression analyses revealed that intrinsic anatomic factors account for most of the variance in strain. Among the extrinsic variables, hip and trunk flexion angles significantly contributed to the strain. The empirical relationship developed in this study could be used to predict the relative strain between jumps of a participant and may be beneficial in developing training programs designed to reduce an athlete's risk of injury. Keywords: ACL; muscle force; musculoskeletal modeling; risk factor; knee injuryDespite the large quantity of research available on non-contact anterior cruciate ligament (ACL) injuries, the contributing factors and their relative contribution to the injury is still under debate. 1 This is in part due to the difficulty of measuring ACL strain in vivo 2 and inability to relate the ACL strain to the possible contributing factors. Unless the relationships between body kinematics, muscle forces and ACL strain is understood, the mechanism of ACL injury will remain unclear. Understanding the mechanics behind these injuries is crucial for injury prevention. Injuries may be prevented if screening and training programs are created for athletes who display at-risk mechanics. [3][4][5] Sagittal plane factors have been identified as important contributors to ACL injury mechanisms. [6][7][8] In addition to these extrinsic biomechanical factors, ACL strain is also dependent on a number of intrinsic anatomic factors such as tibial slope, 9,10 femoral notch width, 11 and ACL size. 12 Although these factors are known correlates with ACL strain, the relative contribution of extrinsic biomechanical and intrinsic anatomical factors is unknown.Pioneering efforts have been made to understand the relationship between knee kinematics, kinetics and ACL strain by surgically placing strain gauges on ligaments in live participants. 13 However, for ethical reasons, such approaches have not been extended to activities that are dynamic in nature. Numerical modelling approaches have been used to address this gap [14][15][16] ; however, model validation is complicated by the lack...

Although registration of sports medicine trials in CTG does not consistently result in publication or disclosure of results at 32 months from the time of study completion, observed publication rates are higher than in other orthopaedic subspecialties. Changes are also frequently made to the final presentation of eligibility criteria and primary and secondary outcomes that are not reflected in the registered trial data.

The goal of this study was to determine what visual information is used to navigate around barriers in a cluttered terrain. Twelve traffic pylons were arranged randomly in a 4.55 x 3.15 m travel area: there were 20 different arrangements. For each arrangement, individuals (N = 6) were positioned in 1 of 3 locations on the outside border with their eyes closed: on verbal command they were instructed to open their eyes and quickly go to 1 of 2 specified goals (2 vertical posts defining a door) located on one edge of the travel area. The movement of the body was tracked using the OPTOTRAK system, with the IREDS placed on a collar worn by the subjects. Experimental data of travel path chosen were compared with those predicted by models that incorporated different types of visual information to control path trajectory. The 6 models basically use 2 different strategies for route selection: reactive control based on visual input about the obstacle encountered in the line-of-sight travel path (Model # 1) and path planning based on different visual information (Model # 2, 3, 4, 5, and 6). The models that involve path planning are grouped into 2 categories: models 2, 3, 4, and 5 need detailed geometrical configuration of the obstacles to plan a route while model 6 plans a route based on identifying and avoiding a cluster of obstacles in the travel path. Two measures were used to compare model performance with the actual travel path: the difference in area between predicted and actual travel path and the number of trials that accurately predicted the number of turns during travel. The results suggest that route selection is not based on reactive control, but does involve path planning. The model that best predicts the travel paths taken by the individuals uses visual information about cluster of obstacles and identification of safe corridors to plan a route.

ObjectivesThis study aimed to identify a threshold in annual surgeon volume associated with increased risk of revision (for any cause) and deep infection requiring surgery following primary elective total knee arthroplasty (TKA).DesignA propensity score matched cohort study.SettingOntario, Canada.Participants169 713 persons who received a primary TKA between 2002 and 2016, with 3-year postoperative follow-up.Main outcome measuresRevision arthroplasty (for any cause), and the occurrence of deep surgical infection requiring surgery.ResultsBased on restricted cubic spline analysis, the threshold for increased probability of revision and deep infection requiring surgery was <70 cases/year. After matching of 51 658 TKA recipients from surgeons performing <70 cases/year to TKA recipients from surgeons with greater than 70 cases/year, patients in the former group had a higher rate of revision (for any cause, 2.23% (95% Confidence Interval (CI) 1.39 to 3.07) vs 1.70% (95% CI 0.85 to 2.55); Hazard Ratio (HR) 1.33, 95% CI 1.21 to 1.47, p<0.0001) and deep infection requiring surgery (1.29% (95% CI 0.44 to 2.14) vs 1.09% (95% CI 0.24 to 1.94); HR 1.33, 95% CI 1.17 to 1.51, p<0.0001).ConclusionsFor primary TKA recipients, cases performed by surgeons who had performed fewer than 70 TKAs in the year prior to the index TKA were at 31% increased relative risk of revision (for any cause), and 18% increased relative risk for deep surgical infection requiring surgery, at 3-year follow-up.