Symptomatic spinal stenosis is a very common problem, and decompression surgery has been shown to be superior to nonoperative treatment in selected patient groups. However, performing an instrumented fusion in addition to decompression may avoid revision and improve outcomes. The aim of the SpInOuT feasibility study was to establish whether a definitive randomized controlled trial (RCT) that accounted for the spectrum of pathology contributing to spinal stenosis, including pelvic incidence-lumbar lordosis (PI-LL) mismatch and mobile spondylolisthesis, could be conducted. As part of the SpInOuT-F study, a pilot randomized trial was carried out across five NHS hospitals. Patients were randomized to either spinal decompression alone or spinal decompression plus instrumented fusion. Patient-reported outcome measures were collected at baseline and three months. The intended sample size was 60 patients.Aims
Methods
Ankle fractures are common injuries and the third most common fragility fracture. In all, 40% of ankle fractures in the frail are open and represent a complex clinical scenario, with morbidity and mortality rates similar to hip fracture patients. They have a higher risk of complications, such as wound infections, malunion, hospital-acquired infections, pressure sores, veno-thromboembolic events, and significant sarcopaenia from prolonged bed rest. A modified Delphi method was used and a group of experts with a vested interest in best practice were invited from the British Foot and Ankle Society (BOFAS), British Orthopaedic Association (BOA), Orthopaedic Trauma Society (OTS), British Association of Plastic & Reconstructive Surgeons (BAPRAS), British Geriatric Society (BGS), and the British Limb Reconstruction Society (BLRS).Aims
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Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.Aims
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