Search

Abstract Purpose The aim of the present study is to define the minimal clinically important difference (MCID) for International Knee Documentation Committee (IKDC) and Knee Injury and Osteoarthritis Outcome Score (KOOS) for patients undergoing tibial tubercle osteotomy (TTO) for either (1) patellofemoral pain or (2) patellar instability. Methods Patients undergoing TTO for either patellofemoral pain or patellar instability by one of two sports medicine fellowship‐trained surgeons at a single institution between September 2014 and May 2023 were included in the study. IKDC and KOOS scores were collected preoperatively and minimum 1 year postoperatively. Distribution‐based methods were used to calculate the MCID. Results Seventy‐seven patients (82 knees) were included, with a median age of 29.3 years (interquartile range [IQR]: 22.2−36.3 years) and a median BMI of 24.5 kg/m2 [IQR: 22.3−28.3 kg/m2]. Fifty‐seven patients (74%) were female, and there were 40 right knees (49%). The median time to IKDC and KOOS score was 1.8 and 1.7 years, respectively. Forty‐five patients (46 knees) underwent TTO for patellofemoral instability, and 32 patients (36 knees) underwent TTO for patellofemoral pain. The MCID was 11.5 for IKDC, 10.2 for KOOS pain, 10.1 for KOOS symptoms, 9.9 for KOOS ADL, 14.2 for KOOS sport and 14.2 for KOOS QoL for patients undergoing TTO for patellofemoral pain. The MCID was 11.2 for IKDC, 10.1 for KOOS pain, 10.6 for KOOS symptoms, 10.2 for KOOS ADL, 16.0 for KOOS sport and 13.2 for KOOS QoL for patients undergoing TTO for patellar instability. Conclusion We define the MCIDs for commonly used patient‐reported outcome measures for patients undergoing TTO for either patellofemoral pain or patellar instability. Level of Evidence Level II.

Abstract Introduction In recent years, there has been a significant increase in the development of artificial intelligence (AI) algorithms aimed at reviewing radiographs after total joint arthroplasty (TJA). This disruptive technology is particularly promising in the context of preoperative planning for revision TJA. Yet, the efficacy of AI algorithms regarding TJA implant analysis has not been examined comprehensively. Methods PubMed, EBSCO, and Google Scholar electronic databases were utilized to identify all studies evaluating AI algorithms related to TJA implant analysis between 1 January 2000, and 27 February 2023 (PROSPERO study protocol registration: CRD42023403497). The mean methodological index for non-randomized studies score was 20.4 ± 0.6. We reported the accuracy, sensitivity, specificity, positive predictive value, and area under the curve (AUC) for the performance of each outcome measure. Results Our initial search yielded 374 articles, and a total of 20 studies with three main use cases were included. Sixteen studies analyzed implant identification, two addressed implant failure, and two addressed implant measurements. Each use case had a median AUC and accuracy above 0.90 and 90%, respectively, indicative of a well-performing AI algorithm. Most studies failed to include explainability methods and conduct external validity testing. Conclusion These findings highlight the promising role of AI in recognizing implants in TJA. Preliminary studies have shown strong performance in implant identification, implant failure, and accurately measuring implant dimensions. Future research should follow a standardized guideline to develop and train models and place a strong emphasis on transparency and clarity in reporting results. Level of Evidence Level III.

Despite effective antiviral therapy for hepatitis C virus (HCV), people who are incarcerated and those returning to the community face challenges in obtaining HCV treatment. We aimed to explore facilitators and barriers to HCV treatment during and after incarceration. From July–November 2020 and June–July 2021, we conducted 27 semi-structured interviews with residents who were formerly incarcerated in jail or prison. The interviews were audio-recorded and professionally transcribed. We used descriptive statistics to characterize the study sample and analyzed qualitative data thematically using an iterative process. Participants included five women and 22 men who self-identified as White (n = 14), Latinx (n = 8), and Black (n = 5). During incarceration, a key facilitator was having sufficient time to complete HCV treatment, and the corresponding barrier was delaying treatment initiation. After incarceration, a key facilitator was connecting with reentry programs (e.g., halfway house or rehabilitation program) that coordinated the treatment logistics and provided support with culturally sensitive staff. Barriers included a lack of insurance coverage and higher-ranking priorities (e.g., managing more immediate reentry challenges such as other comorbidities, employment, housing, and legal issues), low perceived risk of harm related to HCV, and active substance use. Incarceration and reentry pose distinct facilitators and challenges to accessing HCV treatment. These findings signal the need for interventions to improve engagement in HCV care both during and after incarceration to assist in closing the gap of untreated people living with HCV.