Arciero, Emily

Background: In recent years, there has been an increased push for diversity in orthopaedics at the residency level. There has also been a noticeable shift away from foot and ankle fellowship, with many fellowship positions open throughout the country annually. With the increase in osteopathic and female orthopaedic applications to orthopaedic surgery residency, we hypothesized that this would be reflected in the foot and ankle fellowship applications in the last decade. Methods: The San Franscisco Match data from 2012 to 2023 were analyzed for degree type and from 2013 to 2023 for gender. There were 1082 applicants for adult foot and ankle fellowships from 2012 to 2023, 74 osteopathic applicants (11.4%), and 573 allopathic applicants (88.6%). There were 126 female applicants (21.3%) and 465 male applicants (78.7%). The 435 international graduates were excluded from this analysis. The degree type, gender, number of applicants, and number of applicants matched into orthopaedic foot and ankle fellowship was reviewed. Results: The number of osteopathic applicants and female applications did not change significantly over the study period. There was a statistically significant decrease in the number of allopathic applicants ( P = .003) and a significant decrease in the number of male applicants to foot and ankle fellowship ( P = .00004). The match rate for allopathic, osteopathic, and male applicants all statistically increased over the study periods, whereas the female applicant match rate remained stable. Programs tended to rank their matched allopathic residents higher, whereas osteopathic applicants’ position in the fellowship program rank list remained steady. Female and male applicants matched at programs higher on their rank lists, whereas programs match applicants lower on theirs. Conclusion: More orthopaedic foot and ankle surgeons are needed nationwide. Understanding recent trends in applicant demographics and match outcomes may help inform strategies to increase interest and participation in this subspecialty. Level of Evidence: IV, cross-sectional study.

Background: Hip arthroscopy has seen a significant surge in utilization, but complications remain, and optimal functional outcomes are not guaranteed. Artificial intelligence (AI) has emerged as an effective supportive decision-making tool for surgeons. The purpose of this systematic review was to characterize the outcomes, performance, and validity (generalizability) of AI-based prediction models for hip arthroscopy in current literature. Methods: Two reviewers independently completed structured searches using PubMed/MEDLINE and Embase databases on August 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (hip arthroscopy). Studies that investigated AI-based risk prediction models in hip arthroscopy were included. The primary outcomes of interest were the variable(s) predicted by the models, best model performance achieved (primarily based on area under the curve, but also accuracy, etc), and whether the model(s) had been externally validated (generalizable). Results: Seventy-seven studies were identified from the primary search. Thirteen studies were included in the final analysis. Six studies (n = 6,568) applied AI for predicting the achievement of minimal clinically important difference for various patient-reported outcome measures such as the visual analog scale and the International Hip Outcome Tool 12-Item Questionnaire, with area under a receiver-operating characteristic curve (AUC) values ranging from 0.572 to 0.94. Three studies used AI for predicting repeat hip surgery with AUC values between 0.67 and 0.848. Four studies focused on predicting other risks, such as prolonged postoperative opioid use, with AUC values ranging from 0.71 to 0.76. None of the 13 studies assessed the generalizability of their models through external validation. Conclusion: AI is being investigated for predicting clinical outcomes after hip arthroscopy. However, the performance of AI models varies widely, with AUC values ranging from 0.572 to 0.94. Critically, none of the models have undergone external validation, limiting their clinical applicability. Further research is needed to improve model performance and ensure generalizability before these tools can be reliably integrated into patient care. Level of Evidence: Level IV . See Instructions for Authors for a complete description of levels of evidence.