The diagnostic sub-categorization of cauda equina syndrome (CES) is used to aid communication between doctors and other healthcare professionals. It is also used to determine the need for, and urgency of, MRI and surgery in these patients. A recent paper by Hoeritzauer et al (2023) in this journal examined the interobserver reliability of the widely accepted subcategories in 100 patients with cauda equina syndrome. They found that there is no useful interobserver agreement for the subcategories, even for experienced spinal surgeons. This observation is supported by the largest prospective study of the treatment of cauda equina syndrome in the UK by Woodfield et al (2023). If the accepted subcategories are unreliable, they cannot be used in the way that they are currently, and they should be revised or abandoned. This paper presents a reassessment of the diagnostic and prognostic subcategories of cauda equina syndrome in the light of this evidence, with a suggested cure based on a more inclusive synthesis of symptoms, signs, bladder ultrasound scan results, and pre-intervention urinary catheterization. Cite this article: Bone Joint J 2024;106-B(3):227–231

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines.

Cite this article: Bone Joint J 2021;103-B(12):1754–1758.

Musculoskeletal diseases are having a growing impact worldwide. It is therefore crucial to have an evidence base to most effectively and efficiently implement future health services across different healthcare systems. International trials are an opportunity to address these challenges and have many potential benefits. They are, however, complex to set up and deliver, which may impact on the efficient and timely delivery of a project. There are a number of models of how international trials are currently being delivered across a range of orthopaedic patient populations, which are discussed here. The examples given highlight that the key to overcoming these challenges is the development of trusted and equal partnerships with collaborators in each country. International trials have the potential to address a global burden of disease, and in turn optimize the benefit to patients in the collaborating countries and those with similar health services and care systems.

Cite this article: Bone Joint J 2023;105-B(7):719–722.

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.