Chhabra, Anikar

Abstract Osteochondral lesions of the knee, particularly in patients with early osteoarthritic changes, remain a clinical challenge. The Agili‐C implant (CartiHeal, Smith & Nephew), an aragonite‐based osteochondral scaffold, offers a single‐stage, off‐the‐shelf solution designed to regenerate both cartilage and subchondral bone. Unlike cell‐based therapies, Agili‐C eliminates the need for cell harvesting, culturing, or bioreactors, and avoids the morbidity associated with autografts and allografts. Recent multicenter randomized controlled trials have showed superior functional outcomes and pain reduction with Agili‐C compared to microfracture and debridement, even in patients with early osteoarthritis (Kellgren‐Lawrence grades 0‐3). This Technical Note describes the surgical technique for Agili‐C implantation and highlights key technical pearls to optimize outcomes.

Purpose To assess the ability of artificial intelligence (AI) to identify common sports‐related pathologies using radiologic imaging and to compare ChatGPT 4.0 with 2 competitors, Grok 2 and Claude 3.5 Sonnet. Methods ChatGPT 4.0, Grok 2, and Claude 3.5 Sonnet were used. Five common orthopaedic sports pathologies were chosen: anterior cruciate ligament tears, posterior cruciate ligament tears, meniscal tears, chondral pathologies, and rotator cuff tears. Fifty images representing each pathology were collected from a radiologic imaging database when possible, which included radiographic images, computed tomography, and magnetic resonance imaging. Normal images were collected that corresponded to each diagnostic category. Receiver operator characteristic curves and area under the curve values were calculated to assess the accuracy of each AI platform. Results ChatGPT 4.0, Grok 2, and Claude 3.5 Sonnet accurately identified the pathology in 23.6%, 15.7%, and 17.1% of diseased images, respectively. ChatGPT and Grok were most accurate at identifying meniscus pathologies (ChatGPT: 48%, Grok: 42%), whereas Claude Sonnet was most accurate at identifying anterior cruciate ligament pathologies (30%). The area under the curve for ChatGPT, Grok, and Claude Sonnet was 0.21, 0.16, and 0.15, respectively (ChatGPT 4.0 vs Grok 2, P = .30; ChatGPT 4.0 vs Claude 3.5 Sonnet, P = .24; Grok 2 vs Claude 3.5 Sonnet, P > .99). There were no differences in performance between the 3 platforms overall or within any of the diagnostic categories. Conclusions ChatGPT 4.0, Grok 2, and Claude 3.5 Sonnet correctly identified the pathology in less than 25% of images of common sports‐related pathologies and showed area under the curve values well below 0.5, indicating poor accuracy. Based on these findings, we do not recommend the current use of these generative AI models for image‐based diagnosis in orthopaedics. Clinical Relevance As the use of AI becomes more popular within the general public, it becomes increasingly important to make aware the capabilities and limitations of popular AI platforms in regard to their current image‐based diagnostic capabilities.

Purpose To use the All of Us database to investigate barriers to health care access for patients with meniscus tears and to examine how specific demographic, socioeconomic, and health‐related factors independently influence their health care utilization. Methods The National Institutes of Health All of Us database was queried for patient data between March 2017 and March 2023 to form a cohort of patients with meniscal tears. Patients with meniscal tears were identified using Systematized Nomenclature of Medicine clinical terms. All patients with self‐reported meniscus tears were included, with no exclusion criteria to ensure a diverse representation of underrepresented individuals. Patient responses to the “Healthcare Access and Utilization Survey” were analyzed across 4 domains: “delayed care,” “could not afford care,” “skipped medication,” and “have not seen a provider in over one year.” Results In total, 5,671 survey respondents with meniscal tears were included for analysis from a population of 436,000 eligible records. Among them, 27.7% reported delayed care, 23.9% were unable to afford care, 11.8% skipped medication, and 2.0% had not seen a provider in over a year. Patients with annual incomes under $50,000 were more likely to delay care (odds ratio [OR], 1.41; P < .001), be unable to afford care (OR, 1.73; P < .001), or skip medications (OR, 1.79; P < .001). Younger age than the cohort mean of 65.4 years, female sex, and poor physical and mental health were associated with impaired access in at least 1 care domain. Conclusions Over one‐fourth of patients with a diagnosis of meniscus tear who responded to the All of Us “Healthcare Access and Utilization Survey” reported difficulties with access to care across 4 domains, with those of low income, younger age, female sex, and poor mental or physical health experiencing significant barriers to care. Clinical Relevance This research can inform strategies for equitable treatment of meniscal injuries by highlighting disparities in orthopaedic care access and utilization.

Purpose To compare Medicaid and Medicare rates for the 20 most commonly billed orthopaedic sports medicine procedures. Methods Medicaid reimbursement rates were obtained from state‐specific fee schedules. Medicare rates were collected from the Centers for Medicare & Medicaid Services Physician Fee Schedule, along with relative value units for each procedure. State rates were compared using averages and dollar differences. Reimbursement rates were adjusted for cost of living using the Medicare Wage Index. Variability between states and between procedures was assessed using coefficients of variation. Results Mean Medicaid reimbursement was lower than that of Medicare for 19 of the 20 procedures. In total, Medicaid reimbursed surgeons 14.9% less when data were unadjusted and 26.0% less when data were adjusted for cost of living. Variation in Medicare rates between states was constant, with a coefficient of variation of 0.06. For Medicaid, the coefficients of variation ranged from 0.27 to 0.70 for unadjusted data and from 0.35 to 0.69 for adjusted data. Conclusions On average, Medicaid reimburses orthopaedic sports medicine specialists less than does Medicare, with substantial variability between states. Adjustment for cost of living increases the percentage difference and the variability between states’ Medicaid reimbursement rates. Level of Evidence Level IV, economic analysis.

Purpose To characterize the relationship between testosterone replacement therapy (TRT) and anterior cruciate ligament (ACL) injuries. Methods A retrospective cohort study using a large insurance database was conducted. Patients who were prescribed TRT for at least 3 months were matched with controls who were not prescribed TRT. Rates of ACL tears were compared between the cohorts. Multiple subgroups were created based on age (<25, 25‐35, 36‐45, 46‐55, 56‐65, and 65+ years). Multivariable logistic regressions were performed to determine the association of TRT with ACL tears while accounting for demographic variables and comorbidities. Results After matching, there were 160,839 patients in both the TRT cohort and the control cohort. The incidence of ACL injuries was 17.8 per 10,000 person‐years (95% confidence interval [CI]: 16.4‐19.2) for patients who were prescribed TRT and 4.9 per 10,000 person‐years (95% CI: 4.1‐5.7) for controls ( P < .001). Within 2 years of filling a testosterone prescription for at least 3 months, 572 (0.35%) patients experienced an ACL injury compared to only 157 (0.10%) controls during the same follow‐up period (odds ratio: 2.77; 95% CI: 2.26‐3.42, P < .001). When stratified by age, all groups except the <25 years of age group demonstrated a significantly higher rate of ACL tears (odds ratio 3.91‐12.3, P < .001‐.009). When separated by sex, males on TRT were 3.13 (95% CI: 2.50‐3.93, P < .001) times more likely while females on TRT were 1.94 (95% CI: 1.13‐3.41, P = .018) times more likely to experience an ACL injury compared to controls. Conclusions This study found that patients prescribed at least 3 months of TRT had a significantly higher incidence of ACL injuries compared to controls within a 2‐year follow‐up period. Level of Evidence Level III, retrospective comparative study.

Abstract ➤ The COVID-19 pandemic created a persistent surgical backlog in elective orthopedic surgeries. ➤ Artificial intelligence (AI) uses computer algorithms to solve problems and has potential as a powerful tool in health care. ➤ AI can help improve current and future orthopedic backlogs through enhancing surgical schedules, optimizing preoperative planning, and predicting postsurgical outcomes. ➤ AI may help manage existing waitlists and increase efficiency in orthopedic workflows.

Purpose To assess surgeon reimbursement for common arthroscopic procedures, including arthroscopic meniscal debridement and arthroscopic rotator cuff repair, in patients with differing risk profiles within the Medicare population. Methods A publicly available Medicare database was used to identify all cases of arthroscopic meniscal debridement and arthroscopic rotator cuff repair procedures billed to Medicare from 2013 to 2020. The surgeon reimbursement from Medicare was collected and adjusted for inflation. All procedure episodes were split into 2 cohorts; those with a hierarchical condition category (HCC) risk score ≥1.5, and those with patient HCC risk scores <1.5. Reimbursement rates were compared between groups. Results From 2013 to 2020, a total of 624,077 meniscal debridement procedures and 567,794 arthroscopic rotator cuff repairs were billed to Medicare Part B. During this time, the mean adjusted surgeon reimbursement for arthroscopic rotator cuff repair decreased by 9.2% from 2013 to 2020. During the same time period, the adjusted mean surgeon reimbursement for arthroscopic both compartment meniscal debridement and single compartment meniscal debridement decreased by 7.9% and 9.9%, respectively. Throughout the study period, the mean HCC risk score increased from 1.19 in 2013 to 1.31 in 2020 ( P < .001). Across all years in the study, the sicker cohort had a significantly greater rate of all comorbidities and a greater mean body mass index ( P < .001 for all variables). The mean reimbursement across this cohort was lower for both rotator cuff repair ( P = .037) and meniscal debridement procedures ( P < .001) compared with the healthier cohort. Conclusions This study demonstrates that from 2013 to 2020, inflation‐adjusted surgeon reimbursement for arthroscopic rotator cuff repair and meniscal debridement decreased while patient complexity increased. Further, mean surgeon reimbursement was lower among patients with more complexity in comparison with their healthier counterparts for such procedures. Level of Evidence Level III, retrospective cohort study.

Purpose To use Google trends to explore differences in public interest among types of anterior cruciate ligament (ACL) autografts, specifically quadriceps tendon, patellar tendon, and hamstring tendon autografts, between 2008 and 2019. Methods Data were obtained by querying Google Trends for key terms and phrases for online search data ranging from January 2008 to December 2019. Relative search volumes were created based on searches related to ACL reconstruction with comparative analysis generated for search terms related to quadriceps ACL, patellar tendon ACL, and hamstring ACL autografts. Statistical analysis included linear regression analysis, comparison of quarterly search volume trends over time, and comparison of cumulative annual search volumes for 2008 versus 2019. Results Linear models for respective search terms were statistically significant for the quadriceps ( P < .001) and patellar ( P = .007) tendon autograft groups but not the hamstring group ( P = .129). The quadriceps autograft group demonstrated a 12‐year search volume trend change of 0.56, which was significantly greater than the hamstring (0.07; P < .001) and patellar tendon (0.168; P < .001) groups. There was no significant difference in the trend change between hamstring and patellar tendon groups ( P = .20). Percent change in cumulative relative annual search volumes between 2008 and 2019 was 112% for the quadriceps tendon group, 12.9% for the hamstring group, and 18.6% for the patellar tendon group. Conclusions This study indicates a consistently increasing public interest in quadriceps tendon autograft for ACL reconstruction. The quadriceps autograft group demonstrated a significantly greater 12‐year online search volume, greater linear correlation, and larger percent change between 2008 and 2019 compared with patellar tendon or hamstring autograft groups. Clinical Relevance Awareness of patient perceptions has value in informing shared decision‐making, aligning patient expectations, and guiding areas of future research. Each of these has an impact on patient care. Being aware of patient interest and expectations is particularly important in areas with controversial or emerging research.

Purpose To determine the incidence rates and associated risk factors of season‐ending injuries (SEIs) in the National Basketball Association (NBA) from the 2015‐20 seasons. Methods Publicly available records of active NBA players between the 2015‐16 and 2020‐21 seasons were reviewed to identify players with an SEI. In this study, SEI was classified as any injury that resulted in failure to return at least 5 games before the end of the team’s game schedule. Injury data from the 2019‐20 NBA season, shortened because of the coronavirus disease 2019 pandemic, were omitted. The primary outcome was the incidence of SEIs reported per 1,000 game exposures (GEs). Player demographics, basketball statistics, injury characteristics, and timing of injury were recorded. Secondary analysis, including bivariate analysis and multivariate logistic regression, was performed to investigate factors associated with having an SEI. Results In total, 196 players (15.6% of all players) sustained a combined 238 SEIs between the 2015‐16 and 2020‐21 seasons, indicating a rate of 1.74 SEIs per 1,000 GEs. When characterized by body part, knee injuries were found to be the most frequent SEI, at a rate of 0.47 injuries per 1,000 GEs. Accounting for potential confounders, having an SEI was significantly associated with more minutes per game played (odds ratio, 1.06, 95% confidence interval, 0.99‐1.01, P < .001). Conclusions SEIs occurred in 15.6% of players in this study, with an overall rate of 1.74 SEIs per 1,000 GEs. The most significant risk factor associated with injury was minutes per game. SEI was more likely to occur in the third and fourth quartiles of the NBA season than in the first or second quartile. Level of Evidence : Level III, retrospective comparative prognostic investigation.