Timing of surgery for complete proximal hamstring avulsion injuries: successful clinical outcomes at 6 weeks, 6 months, and after 6 months of injury.

BACKGROUND: Avulsion of the proximal hamstring origin is well documented, and surgical treatment is advocated for complete avulsions. PURPOSE: To compare the return to preinjury level of sport and the complexity of surgery in athletes undergoing surgical intervention for complete proximal hamstring avulsions within 6 weeks, 6 months, and after 6 months of injury. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: This was the largest case series from a tertiary referral center reported in the literature. A total of 112 athletes were included, with complete proximal avulsion injuries confirmed on MRI scans; 63 patients (56.3%) were high-level athletes. Patients were divided into subgroups depending on the timing of surgical intervention: "early" was defined within 6 weeks, "delayed" within 6 weeks to 6 months, and "late" after 6 months. All patients were surgically explored and repaired with the aim of comparing the timing between each group and the return to preinjury sport. All patients underwent an individualized rehabilitation protocol. There was no loss to follow-up. The primary outcome measure was the return to preinjury level sports activity. RESULTS: A total of 108 patients (96.4%) returned to sport. In the early intervention group, the average time of return to play was 16 weeks, 9 weeks faster than the delayed group and 13 weeks faster than the late group. There were 2 partial reruptures in those with delayed intervention­both athletes retired from competitive sport but were recreationally active. Two other athletes recovered well but retired from playing at all levels. Twelve athletes (2 in the early intervention group, 5 in the delayed, 5 in the late) were delayed by local nerve symptoms. Only 2 cases required further exploration. CONCLUSION: Early surgical intervention was associated with good clinical outcomes and a quicker return to sport; however, delaying the diagnosis can lead to prolonged morbidity and an increased likelihood of complications.

Modeling and convergence analysis of distributed coevolutionary algorithms.

A theoretical foundation is presented for modeling and convergence analysis of a class of distributed coevolutionary algorithms applied to optimization problems in which the variables are partitioned among p nodes. An evolutionary algorithm at each of the p nodes performs a local evolutionary search based on its own set of primary variables, and the secondary variable set at each node is clamped during this phase. An infrequent intercommunication between the nodes updates the secondary variables at each node. The local search and intercommunication phases alternate, resulting in a cooperative search by the p nodes. First, we specify a theoretical basis for a class of centralized evolutionary algorithms in terms of construction and evolution of sampling distributions over the feasible space. Next, this foundation is extended to develop a model for a class of distributed coevolutionary algorithms. Convergence and convergence rate analyzes are pursued for basic classes of objective functions. Our theoretical investigation reveals that for certain unimodal and multimodal objectives, we can expect these algorithms to converge at a geometrical rate. The distributed coevolutionary algorithms are of most interest from the perspective of their performance advantage compared to centralized algorithms, when they execute in a network environment with significant local access and internode communication delays. The relative performance of these algorithms is therefore evaluated in a distributed environment with realistic parameters of network behavior.