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Study Design: A retrospective cohort study., Objective: To evaluate long-term rotational changes in the vertebrae of patients with adolescent idiopathic scoliosis (AIS) who underwent direct vertebral rotation (DVR)., Summary of Background Data: DVR using thoracic pedicle screws, a rotational corrective maneuver used in the surgical treatment of AIS, was introduced in 2004. Although DVR is considered to be the main axial corrective maneuver, the long-term rotational changes of vertebrae following this treatment are not well understood., Materials and Methods: A total of 135 vertebrae that underwent DVR using thoracic pedicle screws with a minimum five-year follow-up were retrospectively assessed for the vertebral rotation angle. The vertebral rotation of the apical vertebra (AV) and distal end vertebra (EV) was evaluated using the Nash-Moe scale, the rotational angle to the sacrum (RAsac), and the Aaro and Dahlborn method. The Student t test (paired means) was used for continuous variables, and the χ 2 test was used for categorical variables, as appropriate. A comparison of two and three groups used a one-way repeated measures analysis of variance, and the post hoc analysis used the Bonferroni test., Results: The mean Nash-Moe scale of distal EV showed statistically significant differences between preoperative and postoperative values ( P =0.034) and no statistically significant difference between postoperative and last follow-up values ( P =1.000). The last follow-up RAsac of AV did not differ significantly from the preoperative RAsac of AV ( P =0.515). The last follow-up RAsac of distal EV was significantly lower than the preoperative RAsac of distal EV ( P =0.001). The Pearson correlation analysis showed that the last follow-up RAsac of distal EV was correlated with the Cobb angle of the main curve ( r =0.459, P =0.004), loss of correction ( r =0.541, P =0.001), and lowest instrumented vertebra tilt angle ( r =0.504, P =0.001)., Conclusions: The rotation regression phenomenon in AV and rotation maintenance in distal EV were observed after DVR over an average 10-year follow-up. These findings suggest that the DVR in the surgical treatment of AIS has a positive long-term effect on the stabilization of distal EV from the point of view of axial rotation., Level of Evidence: 4., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Due to recent advances in artificial intelligence (AI), language model applications can generate logical text output that is difficult to distinguish from human writing. ChatGPT (OpenAI) and Bard (subsequently rebranded as "Gemini"; Google AI) were developed using distinct approaches, but little has been studied about the difference in their capability to generate the abstract. The use of AI to write scientific abstracts in the field of spine surgery is the center of much debate and controversy., Objective: The objective of this study is to assess the reproducibility of the structured abstracts generated by ChatGPT and Bard compared to human-written abstracts in the field of spine surgery., Methods: In total, 60 abstracts dealing with spine sections were randomly selected from 7 reputable journals and used as ChatGPT and Bard input statements to generate abstracts based on supplied paper titles. A total of 174 abstracts, divided into human-written abstracts, ChatGPT-generated abstracts, and Bard-generated abstracts, were evaluated for compliance with the structured format of journal guidelines and consistency of content. The likelihood of plagiarism and AI output was assessed using the iThenticate and ZeroGPT programs, respectively. A total of 8 reviewers in the spinal field evaluated 30 randomly extracted abstracts to determine whether they were produced by AI or human authors., Results: The proportion of abstracts that met journal formatting guidelines was greater among ChatGPT abstracts (34/60, 56.6%) compared with those generated by Bard (6/54, 11.1%; P<.001). However, a higher proportion of Bard abstracts (49/54, 90.7%) had word counts that met journal guidelines compared with ChatGPT abstracts (30/60, 50%; P<.001). The similarity index was significantly lower among ChatGPT-generated abstracts (20.7%) compared with Bard-generated abstracts (32.1%; P<.001). The AI-detection program predicted that 21.7% (13/60) of the human group, 63.3% (38/60) of the ChatGPT group, and 87% (47/54) of the Bard group were possibly generated by AI, with an area under the curve value of 0.863 (P<.001). The mean detection rate by human reviewers was 53.8% (SD 11.2%), achieving a sensitivity of 56.3% and a specificity of 48.4%. A total of 56.3% (63/112) of the actual human-written abstracts and 55.9% (62/128) of AI-generated abstracts were recognized as human-written and AI-generated by human reviewers, respectively., Conclusions: Both ChatGPT and Bard can be used to help write abstracts, but most AI-generated abstracts are currently considered unethical due to high plagiarism and AI-detection rates. ChatGPT-generated abstracts appear to be superior to Bard-generated abstracts in meeting journal formatting guidelines. Because humans are unable to accurately distinguish abstracts written by humans from those produced by AI programs, it is crucial to exercise special caution and examine the ethical boundaries of using AI programs, including ChatGPT and Bard., (©Hong Jin Kim, Jae Hyuk Yang, Dong-Gune Chang, Lawrence G Lenke, Javier Pizones, René Castelein, Kota Watanabe, Per D Trobisch, Gregory M Mundis Jr, Seung Woo Suh, Se-Il Suk. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.06.2024.)