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:
Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures.Aims
Methods
The aim of this study was to compare a bicruciate-retaining (BCR) total knee arthroplasty (TKA) with a posterior cruciate-retaining (CR) TKA design in terms of kinematics, measured using fluoroscopy and stability as micromotion using radiostereometric analysis (RSA). A total of 40 patients with end-stage osteoarthritis were included in this randomized controlled trial. All patients performed a step-up and lunge task in front of a monoplane fluoroscope one year postoperatively. Femorotibial contact point (CP) locations were determined at every flexion angle and compared between the groups. RSA images were taken at baseline, six weeks, three, six, 12, and 24 months postoperatively. Clinical and functional outcomes were compared postoperatively for two years.Aims
Methods
To calculate how the likelihood of obtaining measurable benefit from hip or knee arthroplasty varies with preoperative patient-reported scores. Existing UK data from 222,933 knee and 209,760 hip arthroplasty patients were used to model an individual’s probability of gaining meaningful improvement after surgery based on their preoperative Oxford Knee or Hip Score (OKS/OHS). A clinically meaningful improvement after arthroplasty was defined as ≥ 8 point improvement in OHS, and ≥ 7 in OKS.Aims
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