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:
Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’). Cite this article:
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:
There is no unified national training system for orthopaedic surgeons in China. With such rapid progress in many aspects of life in China, there is an imminent need for improvement in the training of orthopaedic specialists. Since 2003 the orthopaedic community in Hong Kong has been working in collaboration with their colleagues in mainland China to develop a training system for orthopaedic surgery. We adopted the system from the Royal College of Surgeons of Edinburgh (RCSEd), setting up a trial centre in the Beijing Jishuitan hospital in 2006, with trainers and trainees attaining the standards set by RCSEd and the Hong Kong College of Orthopaedic Surgeons (HKCOS). This trial is ongoing, with the success of two trainees who passed the exit examination in 2010 and became the first Chinese orthopaedic surgeons with a joint fellowship of both the RCSEd and the HKCOS. Following this inaugural success, we are confident that China will develop a training system for orthopaedic surgeons to a consistently high international standard.
