As an alternative to external fixators, intramedullary lengthening nails (ILNs) can be employed for distraction osteogenesis. While previous studies have demonstrated that typical complications of external devices, such as soft-tissue tethering, and pin site infection can be avoided with ILNs, there is a lack of studies that exclusively investigated tibial distraction osteogenesis with motorized ILNs inserted via an antegrade approach. A total of 58 patients (median age 17 years (interquartile range (IQR) 15 to 21)) treated by unilateral tibial distraction osteogenesis for a median leg length discrepancy of 41 mm (IQR 34 to 53), and nine patients with disproportionate short stature treated by bilateral simultaneous tibial distraction osteogenesis, with magnetically controlled motorized ILNs inserted via an antegrade approach, were retrospectively analyzed. The median follow-up was 37 months (IQR 30 to 51). Outcome measurements were accuracy, precision, reliability, bone healing, complications, and patient-reported outcome assessed by the Limb Deformity-Scoliosis Research Society Score (LD-SRS-30).Aims
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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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