Aims. The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons. Methods. Semi-structured in-depth interviews were carried out on a sample of 12 orthopaedic surgeons. Inductive thematic analysis was used to identify key themes. Results. The four intersecting themes identified were: 1) validity in traditional research, 2) confusion around the definition of AI, 3) an inability to validate AI research, and 4) cautious optimism about AI research. Underpinning these themes is the notion of a validity heuristic that is strongly rooted in traditional research teaching and embedded in medical and surgical training. Conclusion. Research involving AI sometimes challenges the accepted traditional evidence-based framework. This can give rise to confusion among orthopaedic surgeons, who may be unable to confidently validate findings. In our study, the impact of this was mediated by cautious optimism based on an ingrained validity heuristic that orthopaedic surgeons develop through their medical training. Adding to this, the integration of AI into everyday life works to reduce suspicion and aid acceptance. Cite this article: Bone Jt Open 2023;4(9):696–703

Introduction. Rising incidence of fracture neck of femur (NOF) are associated with rising geriatric population. Majority of patients are suffering from comorbid factors. Impaired renal function is a common comorbid factor and most of the time it is attributed to an acute renal impairment following the fracture and surgery. Objective of this study was to identify the effect of renal comorbid factors and their probable relative risk for a fracture and compare the results with Asian and European data. Specific objective was to identify a possibility of presence of pretraumatic subclinical chronic renal failure among fractured Sri Lankans. Methodology. Data were collected from fractured patients (N=200) and non-fracture sample for a period of one year. Variables studied were, serum calcium, serum phosphate, blood hemoglobin level, blood urea and serum creatinine. Data were analyzed using binary logistic and multiple regressions, principal component statistical technique using STATA software. Results. The logistic regression of renal co morbid factors with fracture showed that relative risk of occurrence of higher blood urea (> 8.2 mmol/L) in a NOF patient is 3.35 times higher(p value 0.012), relative risks of occurrence of high serum creatinine (> 120 µmol/L) is 3.17 times higher(P value-0.027), risk of low hemoglobin (Hb < 12) is 2.58(p value 0.005) times higher than a non-fracture patient even after adjustments for blood loss following fracture. Results were compared with the Asian and European data. Conclusions. Pretraumatic high blood urea, serum creatinine and low hemoglobin are associated with NOF Sri Lankan patients. Comparative Asian data shows higher figures in Sri Lanka. Studies in Nordic countries and Europe shows higher incidence of renal impairment in fracture neck of femur patients. Further studies are required to assess the presence of pretraumatic sub clinical chronic renal insufficiency in Sri Lanka and in European countries

Purpose. To characterize the knee kinematic profiles of total knee arthroplasty patient knees intraoperatively, before implant insertion, using principal component analysis. Method. Ninety-two patientsreceived Stryker Triathlon total knee arthroplasty (TKA) implants. The Stryker surgical navigation system was used for all surgeries. The system was used to define rigid bodies representing the femur and tibia, and to track the three-dimensional movement of the knee joint during surgery. Each knee was moved through a passive range of knee flexion/extension before and after implantation of the arthroplasty components. The frontal plane (medial-lateral) movement of the knee joint through a range of 10 to 120 degrees of flexion before implantation was calculated for each knee using the joint coordinate system (referred to as the pre-implant knee kinematic curve). Visual inspection of these patterns indicated three predominant curve types: a backward S shape, a backward C shape and a valgus to varus shape. Each curve was subjectively categorized into one of these three categories. Principal component analysis (PCA), a multivariate statistical analysis technique, was applied to the pre-implant knee kinematic pattern data to objectively extract the major patterns of curve types within the 92 knees. Analysis of variance was used to compare the mean differences in PC scores between the curve shape groups to confirm visual categorization. Results. Of the 92 patient curves, 13 had a backwards S shape, 14 had a backwards C shape and 20 had a valgus to varus shape. Forty of the knee were categorized as ‘other’. The first 3 principal components extracted from the knee kinematics curves cumulatively explained 99.7% of the variability in the original data, confirming 3 predominant curve types. The first PC captured an overall measure of varus/valgus throughout the flexion range. The second PC captured an S-shape pattern, and the third PC captured a C-shape pattern. Analysis of variance showed statistically significant differences between the four groups of knees for each PC (p < 0.0001, p <0.0001, p = 0.04 for PC1, 2 and 3 respectively), indicating significant differences between the groups based on the S and C shape patterns. Conclusion. Visual inspection of pre-implant knee kinematic curves indicates that the knees of patients with severe arthritis have different patterns, the predominant patterns being S and C shape patterns. Principal component analysis was used to confirm these patterns quantitatively and to quantitatively show the differences between patient curve types. Principal component analysis is therefore a potentially powerful tool for a computerized characterization of the dynamic pattern of patient knee joints prior to total knee arthroplasty. Ongoing and future work will be used to link these curve types to outcome metrics, joint morphology and surgical technique. This will provide valuable subject-specific knee dynamic information for surgical planning and design