A pilon fracture is a severe ankle joint injury caused by high-energy trauma, typically affecting men of working age. Although relatively uncommon (5% to 7% of all tibial fractures), this injury causes among the worst functional and health outcomes of any skeletal injury, with a high risk of serious complications and long-term disability, and with devastating consequences on patients’ quality of life and financial prospects. Robust evidence to guide treatment is currently lacking. This study aims to evaluate the clinical and cost-effectiveness of two surgical interventions that are most commonly used to treat pilon fractures. A randomized controlled trial (RCT) of 334 adult patients diagnosed with a closed type C pilon fracture will be conducted. Internal locking plate fixation will be compared with external frame fixation. The primary outcome and endpoint will be the Disability Rating Index (a patient self-reported assessment of physical disability) at 12 months. This will also be measured at baseline, three, six, and 24 months after randomization. Secondary outcomes include the Olerud and Molander Ankle Score (OMAS), the five-level EuroQol five-dimenison score (EQ-5D-5L), complications (including bone healing), resource use, work impact, and patient treatment preference. The acceptability of the treatments and study design to patients and health care professionals will be explored through qualitative methods.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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