Aims. While use of large national clinical databases for orthopaedic trauma research has increased dramatically, there has been little study of the differences in populations contained therein. In this study we aimed to compare populations of patients with femoral shaft fractures across three commonly used national databases, specifically with regard to age and comorbidities. Patients and Methods. Patients were identified in the Nationwide Inpatient Sample (NIS), National Surgical Quality Improvement Program (NSQIP) and National Trauma Data Bank (NTDB). . Results. The distributions of age and Charleston comorbidity index (CCI) reflected a predominantly older population with more comorbidities in NSQIP (mean age 71.5; . sd. 15.6), mean CCI 4.9; . sd. 1.9) than in the NTDB (mean age 45.2; . sd. 21.4), mean CCI = 2.1; . sd. 2.0). Bimodal distributions in the NIS population showed a more mixed population (mean age 56.9; . sd.  24.9), mean CCI 3.2; . sd. 2.3). Differences in age and CCI were all statistically significant (p <  0.001). . Conclusion. While these databases have been commonly used for orthopaedic trauma research, differences in the populations they represent are not always readily apparent. Care must be taken to understand fully these differences before performing or evaluating database research, as the outcomes they detail can only be analysed in context. Take home message: Researchers and those evaluating research should be aware that orthopaedic trauma populations contained in commonly studied national databases may differ substantially based on sampling methods and inclusion criteria. Cite this article: Bone Joint J 2016;98-B:425–32

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.

The April 2012 Research Roundup. 360 . looks at who is capable of being an arthroscopist, bupivacaine, triamcinolone and chondrotoxicity, reducing scarring in injured skeletal muscle, horny Goat Weed and the repair of osseous defects, platelet-derived growth factor and fracture healing, the importance of the reserve zone in a child’s growth plate, coping with advanced arthritis, hydroxyapatite and platelet-rich plasma for bone defects, and calcium phosphate and bone regeneration

The poor reporting and use of statistical methods in orthopaedic papers has been widely discussed by both clinicians and statisticians. A detailed review of research published in general orthopaedic journals was undertaken to assess the quality of experimental design, statistical analysis and reporting. A representative sample of 100 papers was assessed for compliance to CONSORT and STROBE guidelines and the quality of the statistical reporting was assessed using a validated questionnaire. Overall compliance with CONSORT and STROBE guidelines in our study was 59% and 58% respectively, with very few papers fulfilling all criteria. In 37% of papers patient numbers were inadequately reported; 20% of papers introduced new statistical methods in the ‘results’ section not previously reported in the ‘methods’ section, and 23% of papers reported no measurement of error with the main outcome measure. Taken together, these issues indicate a general lack of statistical rigour and are consistent with similar reviews undertaken in a number of other scientific and clinical research disciplines. It is imperative that the orthopaedic research community strives to improve the quality of reporting; a failure to do so could seriously limit the development of future research

Objectives. One commonly used rat fracture model for bone and mineral research is a closed mid-shaft femur fracture as described by Bonnarens in 1984. Initially, this model was believed to create very reproducible fractures. However, there have been frequent reports of comminution and varying rates of complication. Given the importance of precise anticipation of those characteristics in laboratory research, we aimed to precisely estimate the rate of comminution, its importance and its effect on the amount of soft callus created. Furthermore, we aimed to precisely report the rate of complications such as death and infection. Methods. We tested a rat model of femoral fracture on 84 rats based on Bonnarens’ original description. We used a proximal approach with trochanterotomy to insert the pin, a drop tower to create the fracture and a high-resolution fluoroscopic imager to detect the comminution. We weighed the soft callus on day seven and compared the soft callus parameters with the comminution status. Results. The mean operating time was 34.8 minutes (. sd. 9.8). The fracture was usable (transverse, mid-shaft, without significant comminution and with displacement < 1 mm) in 74 animals (88%). Of these 74 usable fractures, slight comminution was detected in 47 (63%). In 50 animals who underwent callus manipulation, slight comminution (n = 32) was statistically correlated to the amount of early callus created (r = 0.35, p = 0.015). Two complications occurred: one death and one deep infection. Conclusions. We propose an accurate description of comminution and complications in order to improve experiments on rat femur fracture model in the field of laboratory research. Cite this article: Bone Joint Res 2013;2:149–54

Traditionally, informed consent for clinical research involves the patient reading an approved Participant Information Sheet, considering the information presented and having as much time as they need to discuss the study information with their friends and relatives, their clinical care and the research teams. This system works well in the ‘planned’ or ‘elective’ setting. But what happens if the patient requires urgent treatment for an injury or emergency?. This article reviews the legal framework which governs informed consent in the emergency setting, discusses how the approach taken may vary according to the details of the emergency and the treatment required, and reports on the patients’ view of providing consent following a serious injury. We then provide some practical tips for managing the process of informed consent in the context of injuries and emergencies. Cite this article: Bone Joint J 2017;99-B:147–150

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.