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Survival analyses are a powerful statistical tool used to analyse data when the outcome of interest involves the time until an event. There is an array of models fit for this goal; however, there are subtle differences in assumptions, as well as a number of pitfalls, that can lead to biased results if researchers are unaware of the subtleties. As larger amounts of data become available, and more survival analyses are published every year, it is important that healthcare professionals understand how to evaluate these models and apply them into their practice. Therefore, the purpose of this study was to present an overview of survival analyses, including required assumptions and important pitfalls, as well as examples of their use within orthopaedic surgery., (© 2023. The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).)

Natural language processing (NLP) describes the broad field of artificial intelligence by which computers are trained to understand and generate human language. Within healthcare research, NLP is commonly used for variable extraction and classification/cohort identification tasks. While these tools are becoming increasingly popular and available as both open-source and commercial products, there is a paucity of the literature within the orthopedic space describing the key tasks within these powerful pipelines. Curation and navigation of the electronic medical record are becoming increasingly onerous, and it is important for physicians and other healthcare professionals to understand potential methods of harnessing this large data resource. The purpose of this study is to provide an overview of the tasks required to develop an NLP pipeline for orthopedic research and present recent examples of successful implementations., (© 2022. The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).)

Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning-based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre-processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided. This review strives to provide surgeons with the groundwork needed to identify gaps in the clinical landscape that deep learning models may be able to fill and equips them with the knowledge needed to lead an interdisciplinary team through the process of creating novel deep-learning-based solutions to fill those gaps., (© 2022. The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).)

Mean, median, and mode are among the most basic and consistently used measures of central tendency in statistical analysis and are crucial for simplifying data sets to a single value. However, there is a lack of understanding of when to use each metric and how various factors can impact these values. The aim of this article is to clarify some of the confusion related to each measure and explain how to select the appropriate metric for a given data set. The authors present this work as an educational resource, ensuring that these common statistical concepts are better understood throughout the Orthopedic research community., (© 2022. The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).)

Background: Database research is being used in orthopaedic literature with increased regularity. The main limitation of database research is the absence of diagnosis and treatment verification afforded by medical chart review. This absence may limit the accuracy of some conclusions and recommendations produced by database research. Hypothesis/Purpose: The purpose was to describe the accuracy of 1 database (Rochester Epidemiology Project) used in orthopaedic research to detect isolated anterior cruciate ligament (ACL) tears and to discuss the limitations of database research. It was hypothesized that diagnostic codes alone are unlikely to be accurate in identifying patients with ACL tears., Study Design: Cohort study (diagnosis); Level of evidence, 2., Methods: A population-based historical cohort study was performed with the Rochester Epidemiology Project database. All subjects had International Classification of Diseases, Ninth Revision, diagnosis codes consistent with ACL tears between January 1, 1990, and December 31, 2010. The medical records of all subjects were reviewed in detail to confirm the accuracy of diagnosis and gather data on injury type, laterality, associated meniscal injuries, magnetic resonance imaging findings, and treatment details., Results: A total of 3494 patients had codes consistent with ACL tears, and 2288 of them were confirmed through chart review to have an isolated ACL tear (65.5%). Among these were 1841 patients (52.7%) with an ACL tear within 1 year of injury and an additional 447 (12.8%) with an ACL tear >1 year from injury. Thirty-nine patients (1.1%) had a partial ACL tear diagnosed on magnetic resonance imaging, 48 (1.4%) had an isolated posterior cruciate ligament tear, and 22 (0.6%) had a combined ACL-posterior cruciate ligament injury. Twenty-four patients (0.7%) had ACL reconstruction before the study period. The remaining 1073 patients (30.7%) had diagnostic codes consistent with an ACL tear but did not have a cruciate ligament injury., Conclusion: This study demonstrates low accuracy with the use of diagnostic codes alone to identify an ACL tear. Database studies offer unique benefits to the medical literature, but the inherent limitations should be taken into account when these data are used to counsel patients, dictate clinical management, or make health care policy decisions. Information from a health care database is most accurate when accompanied by verification of diagnosis, treatment, and outcomes with medical chart review.