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
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The aim of this study was to present the first retrieval analysis findings of PRECICE STRYDE intermedullary nails removed from patients, providing useful information in the post-market surveillance of these recently introduced devices. We collected ten nails removed from six patients, together with patient clinical data and plain radiograph imaging. We performed macro- and microscopic analysis of all surfaces and graded the presence of corrosion using validated semiquantitative scoring methods. We determined the elemental composition of surface debris using energy dispersive x-ray spectroscopy (EDS) and used metrology analysis to characterize the surface adjacent to the extendable junctions.Aims
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The imminent introduction of the new Trauma & Orthopaedic (T&O) curriculum, and the implementation of the Improving Surgical Training initiative, reflect yet another paradigm shift in the recent history of trauma and orthopaedic training. The move to outcome-based training without time constraints is a radical departure from the traditional time-based structure and represents an exciting new training frontier. This paper summarizes the history of T&O training reform, explains the rationale for change, and reflects on lessons learnt from the past. Cite this article:
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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