Objective. To compare the effectiveness of arthroscopic partial meniscectomy (APM) and physical therapy (PT) for degenerative meniscus tears. Method. We conducted a literature search through PubMed, Embase, the Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov. Randomized controlled trials in adults with degenerative meniscal tears without symptoms of locking were considered for inclusion. Two researchers independently performed the literature search, assessed the risk of bias, and selected eligible studies. The primary outcome was function at different follow-up time points and the secondary outcome was pain at different follow-up time points. Results. We included 6 randomized controlled trials, with a total of 1006 participants, among which 495 were in the APM group and 511 were in the PT group. We found a small benefit in functional outcomes in the APM group until the 12 months follow-up time point (SMD=0.20; 95%CI=0.0‐0.33; p=0.002; I2=34%), but no significant differences in function between groups at the 24-month follow-up time point (SMD=0.12; 95%CI=−0.04−0.28;p=0.14; I2=28%). There was also small benefit in the APM group until the 12 months follow-up time point for pain (SMD=0.14; 95%CI=0.01−027; p=0.03; I2=36%), but no significant difference in pain between groups at 24 months (SMD=0.11; 95%CI=−0.05−0.28; p=0.18; I2=0%). Conclusion. In the treatment of degenerative meniscus tears, APM yielded better functional and pain outcomes compared with physical therapy in the short term until 12 months, but there were comparable results for pain and functional outcomes between the groups at the 24 months follow-up time point.

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BackgroundLarge Language Models (LLMs) like GPT-4 demonstrate potential applications in diverse areas, including healthcare and patient education. This study evaluates GPT-4’s competency against osteoarthritis (OA) treatment guidelines from the United States and China and assesses its ability in diagnosing and treating orthopedic diseases.MethodsData sources included OA management guidelines and orthopedic examination case questions. Queries were directed to GPT-4 based on these resources, and its responses were compared with the established guidelines and cases. The accuracy and completeness of GPT-4’s responses were evaluated using Likert scales, while case inquiries were stratified into four tiers of correctness and completeness.ResultsGPT-4 exhibited strong performance in providing accurate and complete responses to OA management recommendations from both the American and Chinese guidelines, with high Likert scale scores for accuracy and completeness. It demonstrated proficiency in handling clinical cases, making accurate diagnoses, suggesting appropriate tests, and proposing treatment plans. Few errors were noted in specific complex cases.ConclusionsGPT-4 exhibits potential as an auxiliary tool in orthopedic clinical practice and patient education, demonstrating high accuracy and completeness in interpreting OA treatment guidelines and analyzing clinical cases. Further validation of its capabilities in real-world clinical scenarios is needed.