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Background: The coronoavirus disease 2019 (COVID-19) pandemic had profound impact on elective procedures in the United States. We characterized the longer-term decline and recovery of hip and knee arthroplasty procedures following the onset of the COVID-19 pandemic in the United States. Methods: We conducted a retrospective analysis of patients undergoing primary and revision total knee and hip arthroplasty (TKA and THA) in the United States between 2014 and 2020 using claims from a large national commercial payer data set contaivning deidentified information from patients with commercial health coverage. We calculated the percentage of cases lost by month using a forecast model to predict TKA and THA volumes in the absence of COVID-19. We then calculated the association between COVID-19 positivity rates and THA/TKA procedures by state and month. Results: There was a large initial decline in procedures, with primary TKA and THA volumes declining by 93.2% and 87.1% in April 2020, respectively, with revisions seeing more modest declines. Cases quickly recovered with volumes exceeding expected levels in summer months. However, cumulative 2020 volumes remained below expected with 9.7% and 7.5% of expected primary TKA and THA cases lost, respectively. Higher state COVID-19 positivity rates were associated with lower primary TKA, THA, and revision knee procedure rates. Conclusions: After the initial decline in March and April, knee and hip arthroplasty cases resumed quickly; however, by the end of 2020, the annual procedure volume had still not recovered fully. The loss in case volume within states was worse in months with higher COVID-19 positivity rates.
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The practice of medicine is rapidly transforming as a result of technological breakthroughs. Artificial intelligence (AI) systems are becoming more and more relevant in medicine and orthopaedic surgery as a result of the nearly exponential growth in computer processing power, cloud based computing, and development, and refining of medical-task specific software algorithms. Because of the extensive role of technologies such as medical imaging that bring high sensitivity, specificity, and positive/negative prognostic value to management of orthopaedic disorders, the field is particularly ripe for the application of machine-based integration of imaging studies, among other applications. Through this review, we seek to promote awareness in the orthopaedics community of the current accomplishments and projected uses of AI and ML as described in the literature. We summarize the current state of the art in the use of ML and AI in five key orthopaedic disciplines: joint reconstruction, spine, orthopaedic oncology, trauma, and sports medicine.
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Limited research has examined the demands of backward locomotion at various speeds using common load monitoring metrics in team sport athletes. Consequently, this study compared the external and internal loads between backward and forward locomotion during intermittent exercise in team sport athletes. Semi-professional, male rugby league players (n = 29) completed the same exercise protocol on two occasions in backward and forward directions. On each occasion, participants performed separate 20 m trials at self-selected walking, jogging, running, and sprinting speeds and then completed a 15 min modified Loughborough intermittent shuttle test (mLIST). Common external and internal load metrics were gathered across testing. Faster speeds (p < 0.001) were attained at all speeds during forward locomotion in the 20 m trials. Non-significant differences in accumulated PlayerLoadTM were found between directions across the mLIST; however, higher relative (per min) PlayerLoadTM (p < 0.001) was apparent during backward locomotion when walking and during forward locomotion when sprinting during the mLIST. RPE and mean heart rate were higher (p < 0.001) during backward locomotion across the mLIST. These data highlight the unique loading patterns experienced during backward locomotion and suggest practitioners should consider the discernment in loading imposed between backward and forward locomotion when measuring athlete demands using common metrics.