Effect of the NFL's Super Bowl on emergency department visits for assault-related injuries.

PURPOSE: Through its associations with mass gatherings, alcohol consumption, emotional cues, and gambling, the Super Bowl (SB) has been implicated in increased rates of interpersonal violence and assaults. This study endeavors to investigate the relationship between assault-related injuries, especially intimate partner violence (IPV) and SB. METHOD: A retrospective review of prospectively collected data from the National Electronic Injury Surveillance System (NEISS) spanning 2005 to 2017 was conducted. Assault-related injuries were examined in relation to (1) the 4-day Super Bowl weekend (Friday-Monday), (2) Super Bowl Sunday, and (3) the Super Bowl week (Friday-Thursday) for all years, following the loss of the projected winning team (underdog victories), and losses despite a significant point spread favoring one team (upset losses). National estimates of injuries and associated variables were derived using the SUDAAN software. RESULTS: While there were no significant differences in the overall number of assaults or assault types during the SB weekend (5.6% vs 5.5%; p = 0.31), relative decreases were observed for altercations (21.1% vs 24.8%; p < 0.01), sexual assault (3.4% vs 4.0%; p < 0.01), and IPV (8.3% vs 12.5%; p < 0.01) on the Friday preceding SB, and robbery incidents on SB Sunday (2.1% vs 3.5%; p = 0.01). No changes in the incidence of assault-related injuries were found based on the favored or underdog status of the teams, including upset losses. CONCLUSION: Contrary to expectations, SB was not associated with increased assault-related injuries. This study underscores the need for year-round structural changes in addressing violence rather than relying solely on heightened awareness during specific events.

Quality assessment of machine learning models for diagnostic imaging in orthopaedics: A systematic review.

BACKGROUND: Machine learning (ML) models are emerging at a rapid pace in orthopaedic imaging due to their ability to facilitate timely diagnostic and treatment decision making. However, despite a considerable increase in model development and ML-related publications, there has been little evaluation regarding the quality of these studies. In order to successfully move forward with the implementation of ML models for diagnostic imaging in orthopaedics, it is imperative that we ensure models are held at a high standard and provide applicable, reliable and accurate results. Multiple reporting guidelines have been developed to help authors and reviewers of ML models, such as the Checklist for AI in Medical Imaging (CLAIM) and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Previous investigations of prognostic orthopaedic ML models have reported concerns with regard to the rate of transparent reporting. Therefore, an assessment of whether ML models for diagnostic imaging in orthopaedics adequately and clearly report essential facets of their model development is warranted. PURPOSES: To evaluate (1) the completeness of the CLAIM checklist and (2) the risk of bias according to the QUADAS-2 tool for ML-based orthopaedic diagnostic imaging models. This study sought to identify ML details that researchers commonly fail to report and to provide recommendations to improve reporting standards for diagnostic imaging ML models. METHODS: A systematic review was performed to identify ML-based diagnostic imaging models in orthopaedic surgery. Articles published within the last 5 years were included. Two reviewers independently extracted data using the CLAIM checklist and QUADAS-2 tool, and discrepancies were resolved by discussion with at least two additional reviewers. RESULTS: After screening 7507 articles, 91 met the study criteria. The mean completeness of CLAIM items was 63 % (SD ± 28 %). Among the worst reported CLAIM items were item 28 (metrics of model performance), item 13 (the handling of missing data) and item 9 (data preprocessing steps), with only 2 % (2/91), 8 % (7/91) and 13 % (12/91) of studies correctly reporting these items, respectively. The QUADAS-2 tool revealed that the patient selection domain was at the highest risk of bias: 18 % (16/91) of studies were at high risk of bias and 32 % (29/91) had an unknown risk of bias. CONCLUSIONS: This review demonstrates that the reporting of relevant information, such as handling missing data and data preprocessing steps, by diagnostic ML studies for orthopaedic imaging studies is limited. Additionally, a substantial number of works were at high risk of bias. Future studies describing ML-based models for diagnostic imaging should adhere to acknowledged methodological standards to maximize the quality and applicability of their models.