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.
Economic evaluation provides a framework for assessing the costs and consequences of alternative programmes or interventions. One common vehicle for economic evaluations in the healthcare context is the decision-analytic model, which synthesizes information on parameter inputs (for example, probabilities or costs of clinical events or health states) from multiple sources and requires application of mathematical techniques, usually within a software program. A plethora of decision-analytic modelling-based economic evaluations of orthopaedic interventions have been published in recent years. This annotation outlines a number of issues that can help readers, reviewers, and decision-makers interpret evidence from decision-analytic modelling-based economic evaluations of orthopaedic interventions. Cite this article:
The Canadian Orthopaedic Trauma Society was started in an endeavour to answer the difficult problem of obtaining enough patients to perform top-quality research into fractures. By maintaining a high standard, including randomised study design, inclusivity, open discussion among surgeons and excellent long-term follow-up, this group has become a leader in the
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:
The credibility and creativity of an author may be gauged by the number of scientific papers he or she has published, as well as the frequency of citations of a particular paper reflecting the impact of the data on the area of practice. The object of this study was to identify and analyse the qualities of the top 100 cited papers in orthopaedic surgery. The database of the Science Citation Index of the Institute for Scientific Information (1945 to 2008) was used. A total of 1490 papers were cited more than 100 times, with the top 100 being subjected to further analysis. The majority originated in the United States, followed by the United Kingdom. The top 100 papers were published in seven specific orthopaedic journals. Analysis of the most-cited orthopaedic papers allows us a unique insight into the qualitites, characteristics and clinical innovations required for a paper to attain ‘classic’ status.
Multicentre clinical trials in trauma care are gaining prominence as a means of generating good-quality evidence to inform and influence clinical practice. We believe multicentre trials have an important role to play in supporting evidence-based practice, and further investment in such trials is justified.
