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It has been reported that use of the Latarjet procedure for anterior shoulder instability increased 250% from 2008 to 2019. With this increase in use, it is imperative to minimize complications. At present, most literature focuses on male patients, with scant evidence regarding female patients. Outcomes are similar when female with male patients are compared, but female patients have greater postoperative visits to the emergency department than male patients, which requires specific attention during the postoperative period. In addition, the literature reporting complications in female versus male patients after Latarjet is conflicting; some studies show similar rates of complications, whereas other studies show greater rates of adverse events in female patients. Previous consensus statements recommended (1) careful dissection; (2) identification of the musculocutaneous and axillary nerves; (3) prevention of overlateralization of the graft; (4) the use of tranexamic acid to reduce blood loss; (5) accurate screw placement; and (6) careful preparation of the glenoid neck and coracoid to reduce rates of nonunion or delayed union. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging-based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery. [ABSTRACT FROM AUTHOR]

Management of varus posteromedial rotatory instability (VPMRI) of the elbow presents a challenging problem, sometimes requiring supplemental external fixation or ulnohumeral cross-pinning. A relatively new treatment adjunct for posterolateral rotatory instability of the elbow has become available with the development of the internal joint stabilizer (IJS) implant. However, this has not been studied for VPMRI because the implant is thought to be ineffective at treating this instability when placed in the usual manner on the lateral side of the elbow. We hypothesize that modifying the IJS technique to place the implant medially will make it as effective as external fixation at preventing VPMRI. Nine whole-arm cadaveric specimens were tested on a rig that allowed for gravity stress of the elbow at 60° of abduction. Intact specimens (INT) were tested to find the baseline stability with all structures intact. Then, O'Driscoll type 2–subtype III coronoid fractures were created with a microsagittal saw, and these same specimens were again tested without any fixation (WAF), with a static lateral external fixator (SLEF), and with a medially placed IJS (MIJS). The method for medial IJS placement is detailed within. The WAF specimens were significantly more unstable than the INT, SLEF, and MIJS groups (P <.001). There was no difference in stability between the INT and the SLEF (P =.59) or MIJS group (P =.21). In this cadaveric model, a medially placed IJS was as effective as an SLEF at maintaining elbow stability in a coronoid-deficient elbow. This technique allows for early elbow range of motion, with no external hardware, and may eliminate the complications associated with external fixation or ulnohumeral cross-pinning. [ABSTRACT FROM AUTHOR]

To analyze the quality and readability of information regarding shoulder stabilization surgery available using an online AI software (ChatGPT), using standardized scoring systems, as well as to report on the given answers by the AI. An open AI model (ChatGPT) was used to answer 23 commonly asked questions from patients on shoulder stabilization surgery. These answers were evaluated for medical accuracy, quality, and readability using The JAMA Benchmark criteria, DISCERN score, Flesch-Kincaid Reading Ease Score (FRES) & Grade Level (FKGL). The JAMA Benchmark criteria score was 0, which is the lowest score, indicating no reliable resources cited. The DISCERN score was 60, which is considered a good score. The areas that open AI model did not achieve full marks were also related to the lack of available source material used to compile the answers, and finally some shortcomings with information not fully supported by the literature. The FRES was 26.2, and the FKGL was considered to be that of a college graduate. There was generally high quality in the answers given on questions relating to shoulder stabilization surgery, but there was a high reading level required to comprehend the information presented. However, it is unclear where the answers came from with no source material cited. It is important to note that the ChatGPT software repeatedly references the need to discuss these questions with an orthopaedic surgeon and the importance of shared discussion making, as well as compliance with surgeon treatment recommendations. As shoulder instability is an injury that predominantly affects younger individuals who may use the Internet for information, this study shows what information patients may be getting online. [ABSTRACT FROM AUTHOR]