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:
Periprosthetic joint infection (PJI) remains an extremely challenging complication. We have focused on this issue more over the last decade than previously, but there are still many unanswered questions. We now have a workable definition that everyone should align to, but we need to continue to focus on identifying the organisms involved. Surgical strategies are evolving and care is becoming more patient-centred. There are some good studies under way. There are, however, still numerous problems to resolve, and the challenge of PJI remains a major one for the orthopaedic community. This annotation provides some up-to-date thoughts about where we are, and the way forward. There is still scope for plenty of research in this area. 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.
