Salter-Harris II fractures of the distal tibia affect children frequently, and when they are displaced present a treatment dilemma. Treatment primarily aims to restore alignment and prevent premature physeal closure, as this can lead to angular deformity, limb length difference, or both. Current literature is of poor methodological quality and is contradictory as to whether conservative or surgical management is superior in avoiding complications and adverse outcomes. A state of clinical equipoise exists regarding whether displaced distal tibial Salter-Harris II fractures in children should be treated with surgery to achieve anatomical reduction, or whether cast treatment alone will lead to a satisfactory outcome. Systematic review and meta-analysis has concluded that high-quality prospective multicentre research is needed to answer this question. The Outcomes of Displaced Distal tibial fractures: Surgery Or Casts in KidS (ODD SOCKS) trial, funded by the National Institute for Health and Care Research, aims to provide this high-quality research in order to answer this question, which has been identified as a top-five research priority by the British Society for Children’s Orthopaedic Surgery. Cite this article:
Prediction tools are instruments which are commonly used to estimate the prognosis in oncology and facilitate clinical decision-making in a more personalized manner. Their popularity is shown by the increasing numbers of prediction tools, which have been described in the medical literature. Many of these tools have been shown to be useful in the field of soft-tissue sarcoma of the extremities (eSTS). In this annotation, we aim to provide an overview of the available prediction tools for eSTS, provide an approach for clinicians to evaluate the performance and usefulness of the available tools for their own patients, and discuss their possible applications in the management of patients with an eSTS. Cite this article:
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
Many aspects of total knee arthroplasty have
changed since its inception. Modern prosthetic design, better fixation techniques,
improved polyethylene wear characteristics and rehabilitation, have
all contributed to a large change in revision rates. Arthroplasty
patients now expect longevity of their prostheses and demand functional
improvement to match. This has led to a re-examination of the long-held
belief that mechanical alignment is instrumental to a successful
outcome and a focus on restoring healthy joint kinematics. A combination
of kinematic restoration and uncemented, adaptable fixation may
hold the key to future advances. Cite this article:
