Over-The-Top Technique for Revision ACL Reconstruction with Achilles Allograft and Associated Lateral Extra-articular Tenodesis

Achilles Allograft; Lateral Extra-articular TenodesisAloinjerto de Aquiles; Tenodesis extraarticular lateralAl·lograft d'Aquil·les; Tenodesi extraarticular lateralRevision anterior cruciate ligament reconstruction (ACL-R) is made challenging by the frequent presence of rotatory instability, tunnel malpositioning and widening, and limited autograft options. Lateral extra-articular tenodesis (LET), alternative tunnel routing, and the use of allograft tissue can be used to manage these challenges. This Technical Note describes revision ACL-R using the over-the-top (OTT) technique with Achilles tendon allograft with concomitant LET. The surgical approach involves routing the graft around the posterior aspect of the lateral femoral condyle, and then deep to the iliotibial band to a site just medial to Gerdy’s tubercle, with staple fixation on the lateral femur for the ACL-R and anterolateral tibia for the LET. The OTT technique with LET provides a versatile approach for the management of failed ACL-R by circumventing challenges in revision ACL-R and addressing rotatory instability, a contributing factor to prior graft failure

Accelerated evidence synthesis in orthopaedics—the roles of natural language processing, expert annotation and large language models

peer reviewedIn an era of electronical medical records, rapidly expanding publication rates of medical knowledge, and large-scale registries, orthopaedics is in a dire need of innovative approaches to facilitate the adoption of the latest knowledge in clinical practice. While machine learning (ML) has been heralded as one solution to many research tasks hampered by previous technological limitations [12], there is an increasing need to direct our attention towards subdomains of ML that are convenient for the extraction of meaningful clinical information stored in medical records. We believe natural language processing (NLP) to be one such domain of ML, with an immense future potential to catalyse rate-limiting steps in orthopaedic research

A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges.

peer reviewedArtificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV

Evolving evidence in the treatment of primary and recurrent posterior cruciate ligament injuries, part 1: anatomy, biomechanics and diagnostics

The posterior cruciate ligament (PCL) represents an intra-articular structure composed of two distinct bundles. Considering the anterior and posterior meniscofemoral ligaments, a total of four ligamentous fibre bundles of the posterior knee complex act synergistically to restrain posterior and rotatory tibial loads. Injury mechanisms associated with high-energy trauma and accompanying injury patterns may complicate the diagnostic evaluation and accuracy. Therefore, a thorough and systematic diagnostic workup is necessary to assess the severity of the PCL injury and to initiate an appropriate treatment approach. Since structural damage to the PCL occurs in more than one third of trauma patients experiencing acute knee injury with hemarthrosis, background knowledge for management of PCL injuries is important. In Part 1 of the evidence-based update on management of primary and recurrent PCL injuries, the anatomical, biomechanical, and diagnostic principles are presented. This paper aims to convey the anatomical and biomechanical knowledge needed for accurate diagnosis to facilitate subsequent decision-making in the treatment of PCL injuries.Level of evidence V

Evolving evidence in the treatment of primary and recurrent posterior cruciate ligament injuries, part 2: surgical techniques, outcomes and rehabilitation

Isolated and combined posterior cruciate ligament (PCL) injuries are associated with severe limitations in daily, professional, and sports activities as well as with devastating long-term effects for the knee joint. As the number of primary and recurrent PCL injuries increases, so does the body of literature, with high-quality evidence evolving in recent years. However, the debate about the ideal treatment approach such as; operative vs. non-operative; single-bundle vs. double-bundle reconstruction; transtibial vs. tibial inlay technique, continues. Ultimately, the goal in the treatment of PCL injuries is restoring native knee kinematics and preventing residual posterior and combined rotatory knee laxity through an individualized approach. Certain demographic, anatomical, and surgical risk factors for failures in operative treatment have been identified. Failures after PCL reconstruction are increasing, confronting the treating surgeon with challenges including the need for revision PCL reconstruction. Part 2 of the evidence-based update on the management of primary and recurrent PCL injuries will summarize the outcomes of operative and non-operative treatment including indications, surgical techniques, complications, and risk factors for recurrent PCL deficiency. This paper aims to support surgeons in decision-making for the treatment of PCL injuries by systematically evaluating underlying risk factors, thus preventing postoperative complications and recurrent knee laxity. LEVEL OF EVIDENCE: V

ChatGPT can yield valuable responses in the context of orthopaedic trauma surgery

Purpose: To assess the possibility of using Generative Pretrained Transformer (ChatGPT) specifically in the context of orthopaedic trauma surgery by questions posed to ChatGPT and to evaluate responses (correctness, completeness and adaptiveness) by orthopaedic trauma surgeons. Methods: ChatGPT (GPT-4 of 12 May 2023) was asked to address 34 common orthopaedic trauma surgery-related questions and generate responses suited to three target groups: patient, nonorthopaedic medical doctor and expert orthopaedic surgeon. Three orthopaedic trauma surgeons independently assessed ChatGPT's responses by using a three-point response scale with a response range between 0 and 2, where a higher number indicates better performance (correctness, completeness and adaptiveness). Results: A total of 18 (52.9%) of all responses were assessed to be correct (2.0) for the patient target group, while 22 (64.7%) and 24 (70.5%) of the responses were determined to be correct for nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. Moreover, a total of 18 (52.9%), 25 (73.5%) and 28 (82.4%) of the responses were assessed to be complete (2.0) for patients, nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. The average adaptiveness was 1.93, 1.95 and 1.97 for patients, nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. Conclusion: The study results indicate that ChatGPT can yield valuable and overall correct responses in the context of orthopaedic trauma surgery across different target groups, which encompassed patients, nonorthopaedic medical surgeons and expert orthopaedic surgeons. The average correctness scores, completeness levels and adaptiveness values indicated the ability of ChatGPT to generate overall correct and complete responses adapted to the target group

Delayed multiligament PCL reconstruction is associated with a higher prevalence of intraarticular injury and may influence treatment

Abstract Background The aim of this study was to investigate differences in concomitant injury patterns and their treatment in patients undergoing early (≤ 12 weeks) and delayed (> 12 weeks) primary multiligament posterior cruciate ligament (PCL) reconstruction (PCL-R). Methods This study was a retrospective chart review of patients undergoing primary multiligament PCL-R at a single institution between 2008 and 2020. Multiligament PCL-R was defined as PCL-R and concurrent surgical treatment of one or more additional knee ligament(s). Exclusion criteria included isolated PCL-R, PCL repair, and missing data for any variable. Patients were dichotomized into early (≤ 12 weeks) and delayed (> 12 weeks) PCL-R groups based on the time elapsed between injury and surgery. Between-group comparison of variables were conducted with the Chi-square, Fisher’s exact, and independent samples t-tests. Results A total of 148 patients were eligible for analysis, with 57 (38.5%) patients in the early and 91 (61.1%) patients in the delayed multiligament PCL-R groups. Concomitant LCL/PLC reconstruction (LCL-R/PLC-R) was performed in 55 (60%) of delayed multiligament PCL-Rs and 23 (40%) of early PCL-Rs (p = 0.02). Despite similar rates of meniscus injury, concomitant meniscus surgery was significantly more prevalent in the early (n = 25, 44%) versus delayed (n = 19, 21%) multiligament PCL-R group (p = 0.003), with a significantly greater proportion of medial meniscus surgeries performed in the early (n = 16, 28%) compared to delayed (n = 13, 14%) PCL-R group (p = 0.04). The prevalence of knee cartilage injury was significantly different between the early (n = 12, 24%) and delayed (n = 41, 46%) multiligament PCL-R groups (p = 0.01), with more frequent involvement of the lateral (n = 17, 19% vs. n = 3, 5%, respectively; p = 0.04) and medial (n = 31, 34% vs. n = 6, 11%, respectively; p = 0.005) femoral condyles in the delayed compared to the early PCL-R group. Conclusions Given higher rates of chondral pathology and medial meniscus surgery seen in delayed multiligament PCL-R, early management of PCL-based multiligament knee injury is recommended to restore knee stability and potentially prevent the development of further intraarticular injury. Level of evidence Level III

A practical guide to the implementation of AI in orthopaedic research – part 1: opportunities in clinical application and overcoming existing challenges

Abstract Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision‐making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI‐intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI‐intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease‐ or injury‐specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high‐risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence I