Biomechanical Evaluation of Suture Tape Internal Brace Reinforcement of Soft Tissue Allografts for ACL Reconstruction Using a Porcine Model.

Background: Internal bracing of anterior cruciate ligament (ACL) surgery is a newer concept gaining popularity. Purpose/Hypothesis: To assess the biomechanical performance of soft tissue ACL reconstruction allografts reinforced with suture tape. It was hypothesized that load to failure would increase and cyclic displacement would decrease at time zero in the constructs reinforced with internal brace suture tape compared with those without suture tape augmentation. Study Design: Controlled laboratory study. Methods: We performed ACL reconstruction on porcine knees using bovine extensor tendon soft tissue allografts: 10 knees without (control) and 10 knees with (reinforced) suture tape reinforcement. An all-inside reconstruction technique was utilized with retrograde tunnel creation. An adjustable-loop device was used for femoral and tibial fixation of all grafts. The suture tape was placed through the tension loop in the femoral fixation construct and independently fixed in the tibia with an interference screw anchor. For each specimen, the authors recorded ultimate load, yield load, stiffness, cyclic displacement, and mode of failure. Outcomes between groups were compared using the Student t test. Results: There was a 33% decrease in mean cyclic displacement in the specimens with reinforced grafts (reinforced vs control: 3.9 ± 0.7 vs 5.8 ± 1.5 mm; P = .001). The reinforced grafts also had a 22% higher mean ultimate load (921 ± 180 vs 717 ± 122 N; P = .008) and a 25% higher mean yield load (808 ± 201 vs 602 ± 155 N; P = .020). There was no significant difference in stiffness between the reinforced versus nonreinforced grafts (136 ± 16 vs 132 ± 18 N/mm; P = .617). Three of the 10 control specimens failed at the graft, compared with 1 of 10 reinforced grafts. All other constructs in both groups failed at the tibial fixation site. Conclusion: Suture tape reinforcement of soft tissue grafts significantly decreased cyclic displacement while significantly increasing ultimate and yield loads without increasing graft construct stiffness during biomechanical testing at time zero in a porcine animal model. Clinical Relevance: The improved biomechanical performance of suture tape-reinforced graft constructs could allow patients to participate in earlier advancement of aggressive rehabilitation and potentially reduce failure rates as graft remodeling progresses.

Semisupervised multitask learning.

Context plays an important role when performing classification, and in this paper we examine context from two perspectives. First, the classification of items within a single task is placed within the context of distinct concurrent or previous classification tasks (multiple distinct data collections). This is referred to as multi-task learning (MTL), and is implemented here in a statistical manner, using a simplified form of the Dirichlet process. In addition, when performing many classification tasks one has simultaneous access to all unlabeled data that must be classified, and therefore there is an opportunity to place the classification of any one feature vector within the context of all unlabeled feature vectors; this is referred to as semi-supervised learning. In this paper we integrate MTL and semi-supervised learning into a single framework, thereby exploiting two forms of contextual information. Example results are presented on a "toy" example, to demonstrate the concept, and the algorithm is also applied to three real data sets.

Kernel-matching pursuits with arbitrary loss functions.

The purpose of this research is to develop a classifier capable of state-of-the-art performance in both computational efficiency and generalization ability while allowing the algorithm designer to choose arbitrary loss functions as appropriate for a give problem domain. This is critical in applications involving heavily imbalanced, noisy, or non-Gaussian distributed data. To achieve this goal, a kernel-matching pursuit (KMP) framework is formulated where the objective is margin maximization rather than the standard error minimization. This approach enables excellent performance and computational savings in the presence of large, imbalanced training data sets and facilitates the development of two general algorithms. These algorithms support the use of arbitrary loss functions allowing the algorithm designer to control the degree to which outliers are penalized and the manner in which non-Gaussian distributed data is handled. Example loss functions are provided and algorithm performance is illustrated in two groups of experimental results. The first group demonstrates that the proposed algorithms perform equivalent to several state-of-the-art machine learning algorithms on well-published, balanced data. The second group of results illustrates superior performance by the proposed algorithms on imbalanced, non-Gaussian data achieved by employing loss functions appropriate for the data characteristics and problem domain.