Aims. Stiffness is a common complication after total knee arthroplasty (TKA). Pathogenesis is not understood, treatment options are limited, and diagnosis is challenging. The aim of this study was to investigate if MRI can be used to visualize intra-articular scarring in patients with stiff, painful knee arthroplasties. Methods. Well-functioning primary TKAs (n = 11), failed non-fibrotic TKAs (n = 5), and patients with a clinical diagnosis of
Desmoid tumours are a rare fibroblastic proliferation of monoclonal origin, arising in deep soft-tissues. Histologically, they are characterized by locally aggressive behaviour and an inability to metastasize, and clinically by a heterogeneous and unpredictable course. Desmoid tumours can occur in any anatomical site, but commonly arise in the limbs. Despite their benign nature, they can be extremely disabling and sometimes life-threatening, causing severe pain and functional limitations. Their surgical management is complex and challenging, due to uncertainties surrounding the biological and clinical behaviour, rarity, and limited available literature. Resection has been the first-line approach for patients with a desmoid tumour but, during the last few decades, a shift towards a more conservative approach has occurred, with an initial ‘wait and see’ policy. Many medical and regional forms of treatment are also available for the management of this condition, and others have recently emerged with promising results. However, many areas of controversy remain, and further studies and global collaboration are needed to obtain prospective and randomized data, in order to develop an appropriate shared stepwise approach. 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:
The aim of this study was to develop and internally validate a prognostic nomogram to predict the probability of gaining a functional range of motion (ROM ≥ 120°) after open arthrolysis of the elbow in patients with post-traumatic stiffness of the elbow. We developed the Shanghai Prediction Model for Elbow Stiffness Surgical Outcome (SPESSO) based on a dataset of 551 patients who underwent open arthrolysis of the elbow in four institutions. Demographic and clinical characteristics were collected from medical records. The least absolute shrinkage and selection operator regression model was used to optimize the selection of relevant features. Multivariable logistic regression analysis was used to build the SPESSO. Its prediction performance was evaluated using the concordance index (C-index) and a calibration graph. Internal validation was conducted using bootstrapping validation.Aims
Methods