Disparity in sex in ankle fracture treatment.

BACKGROUND: Literature has shown implicit bias in the treatment between non-operative and surgical treatment in patients with certain types of ankle fractures, which comprise 7.6% of all adult fractures. An understanding of any bias across all ankle fracture management may prove to be critical for the understanding of potential correlations between treatment methods and outcomes of patients with ankle fractures. Therefore, this study aimed to determine whether there is a sex-based bias in the operative and non-operative treatment of all ankle fractures. METHODS: A retrospective study of 1175 adult patients with ankle fractures was conducted. Data extracted included sex, race, age, type of treatment (non-operative/operative), fracture type (displaced/non-displaced), fracture class, BMI, and length of hospital stay. Odds ratio (OR), Chi-squared, t-test, and Pearson's correlation tests were used with p < 0.05 considered significant. RESULTS: The study population consisted of 750 females (63.8%) and 425 males (36.2%). The study demonstrated a sex-based disparity in operative and non-operative treatment revealing that women are less likely than men to receive operative treatment for displaced ankle fractures (OR = 0.7, 95% CI: 0.5-0.9, p = 0.01). Of the 750 females, 417 (55.6%) underwent non-operative treatment, while 333 (44.4%) females had an operation. Of the 425 males, 204 (48%) had non-operative treatment, while 221 (52%) underwent operative treatment. The distribution of ankle fracture classes between both sexes was similar, suggesting fracture class did not influence the observed disparity. CONCLUSION: Our results suggest sex correlates with the treatment type for ankle fractures, with women more likely to receive non-operative treatment for displaced fractures. As post-treatment outcomes often reflect the chosen form of treatment, it is imperative to determine if a disparity in sex explicates differences in clinical outcomes.

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.

Machine learning prediction models in orthopedic surgery: A systematic review in transparent reporting.

Machine learning (ML) studies are becoming increasingly popular in orthopedics but lack a critically appraisal of their adherence to peer-reviewed guidelines. The objective of this review was to (1) evaluate quality and transparent reporting of ML prediction models in orthopedic surgery based on the transparent reporting of multivariable prediction models for individual prognosis or diagnosis (TRIPOD), and (2) assess risk of bias with the Prediction model Risk Of Bias ASsessment Tool. A systematic review was performed to identify all ML prediction studies published in orthopedic surgery through June 18th, 2020. After screening 7138 studies, 59 studies met the study criteria and were included. Two reviewers independently extracted data and discrepancies were resolved by discussion with at least two additional reviewers present. Across all studies, the overall median completeness for the TRIPOD checklist was 53% (interquartile range 47%-60%). The overall risk of bias was low in 44% (n = 26), high in 41% (n = 24), and unclear in 15% (n = 9). High overall risk of bias was driven by incomplete reporting of performance measures, inadequate handling of missing data, and use of small datasets with inadequate outcome numbers. Although the number of ML studies in orthopedic surgery is increasing rapidly, over 40% of the existing models are at high risk of bias. Furthermore, over half incompletely reported their methods and/or performance measures. Until these issues are adequately addressed to give patients and providers trust in ML models, a considerable gap remains between the development of ML prediction models and their implementation in orthopedic practice.