Safety concerns surrounding osseointegration are a significant barrier to replacing socket prosthesis as the standard of care following limb amputation. While implanted osseointegrated prostheses traditionally occur in two stages, a one-stage approach has emerged. Currently, there is no existing comparison of the outcomes of these different approaches. To address safety concerns, this study sought to determine whether a one-stage osseointegration procedure is associated with fewer adverse events than the two-staged approach. A comprehensive electronic search and quantitative data analysis from eligible studies were performed. Inclusion criteria were adults with a limb amputation managed with a one- or two-stage osseointegration procedure with follow-up reporting of complications.Aims
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This study aims to enhance understanding of clinical and radiological consequences and involved mechanisms that led to corrosion of the Precice Stryde (Stryde) intramedullary lengthening nail in the post market surveillance era of the device. Between 2018 and 2021 more than 2,000 Stryde nails have been implanted worldwide. However, the outcome of treatment with the Stryde system is insufficiently reported. This is a retrospective single-centre study analyzing outcome of 57 consecutive lengthening procedures performed with the Stryde nail at the authors’ institution from February 2019 until November 2020. Macro- and microscopic metallographic analysis of four retrieved nails was conducted. To investigate observed corrosion at telescoping junction, scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDX) were performed.Aims
Methods
The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
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