The recently published Prophylactic Antibiotic Regimens In Tumor Surgery (PARITY) trial found no benefit in extending antibiotic prophylaxis from 24 hours to five days after endoprosthetic reconstruction for lower limb bone tumours. PARITY is the first randomized controlled trial in orthopaedic oncology and is a huge step forward in understanding antibiotic prophylaxis. However, significant gaps remain, including questions around antibiotic choice, particularly in the UK, where cephalosporins are avoided due to concerns of Clostridioides difficile infection. We present a review of the evidence for antibiotic choice, dosing, and timing, and a brief description of PARITY, its implication for practice, and the remaining gaps in our understanding.

Cite this article: Bone Joint J 2023;105-B(8):850–856.

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks.

Cite this article: Bone Joint J 2021;103-B(9):1442–1448.

Continuous technical improvement in spinal surgical procedures, with the aim of enhancing patient outcomes, can be assisted by the deployment of advanced technologies including navigation, intraoperative CT imaging, and surgical robots. The latest generation of robotic surgical systems allows the simultaneous application of a range of digital features that provide the surgeon with an improved view of the surgical field, often through a narrow portal.

There is emerging evidence that procedure-related complications and intraoperative blood loss can be reduced if the new technologies are used by appropriately trained surgeons. Acceptance of the role of surgical robots has increased in recent years among a number of surgical specialities including general surgery, neurosurgery, and orthopaedic surgeons performing major joint arthroplasty. However, ethical challenges have emerged with the rollout of these innovations, such as ensuring surgeon competence in the use of surgical robotics and avoiding financial conflicts of interest. Therefore, it is essential that trainees aspiring to become spinal surgeons as well as established spinal specialists should develop the necessary skills to use robotic technology safely and effectively and understand the ethical framework within which the technology is introduced.

Traditional and more recently developed platforms exist to aid skill acquisition and surgical training which are described.

The aim of this narrative review is to describe the role of surgical robotics in spinal surgery, describe measures of proficiency, and present the range of training platforms that institutions can use to ensure they employ confident spine surgeons adequately prepared for the era of robotic spinal surgery.

Cite this article: Bone Joint J 2020;102-B(5):568–572.

Aims

In 2013, we introduced a specialized, centralized, and interdisciplinary team in our institution that applied a standardized diagnostic and treatment algorithm for the management of prosthetic joint infections (PJIs). The hypothesis for this study was that the outcome of treatment would be improved using this approach.

‘Big data’ is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Billions of dollars have been spent on attempts to build predictive tools from large sets of poorly controlled healthcare metadata. Companies often sell reports at a physician or facility level based on various flawed data sources, and comparative websites of ‘publicly reported data’ purport to educate the public. Physicians should be aware of concerns and pitfalls seen in such data definitions, data clarity, data relevance, data sources and data cleaning when evaluating analytic reports from metadata in health care.

Cite this article: Bone Joint J 2017;99-B:1571–6.

Aims

The importance of accurate identification and reporting of surgical site infection (SSI) is well recognised but poorly defined. Public Health England (PHE) mandated collection of orthopaedic SSI data in 2004. Data submission is required in one of four categories (hip prosthesis, knee prosthesis, repair of neck of femur, reduction of long bone fracture) for one quarter per year. Trusts are encouraged to carry out post-discharge surveillance but this is not mandatory. Recent papers in the orthopaedic literature have highlighted the importance of SSI surveillance and the heterogeneity of surveillance methods. However, details of current orthopaedic SSI surveillance practice has not been described or quantified.