ABSTRACT
Emerging technologies are changing hand surgery by improving surgical precision, minimizing tissue disruption, and expediting patient recovery. These advancements have the potential to revolutionize surgical procedures, patient outcomes, and rehabilitation processes. However, there are still challenges that need to be addressed before these technologies can be widely adopted. These challenges include the learning curve for surgeons, high costs, and ethical considerations. Future research should focus on addressing the limitations of these technologies, exploring their long-term effects, and evaluating their cost-effectiveness. To successfully implement them, a collaborative approach involving clinicians, researchers, engineers, and policymakers is necessary. This review provides an overview of current and future trends in emerging technologies for hand orthopedic surgery.
ABSTRACT
BACKGROUND: To better assess the risk of distal radial fracture in the general population, we need models that take into account a wide range of risk factors other than osteoporosis. The objective was to develop and validate a model for association of patients' characteristics with distal radial fracture that effectively incorporates multifactorial aspects and includes comorbidities. METHOD: We analyzed data from a large Longitudinal Health Insurance Database between 2000 and 2013. The outcome of the study was the occurrence of distal radial fracture and the predictors were demographic and comorbidity data. Two machine learning models were developed and validated for patients ≥50 (N = 2745) and <50 (N = 1587) years of age. RESULTS: For patients aged ≥50 years, selected characteristics included sex, age, urbanization level, osteoarthritis, carpal tunnel syndrome, obesity, hyperlipidemia, trigger finger, hypertension, hypothyroidism, diabetes, hyperthyroidism, and rheumatoid arthritis. For patients <50 years old, selected characteristics included age, sex, diabetes mellitus, urbanization level, carpal tunnel syndrome, hyperlipidemia, osteoarthritis, obesity, and hypertension. Accuracy, sensitivity, specificity, area under the curve, and likelihood ratio were 0.77, 0.83, 0.72, 0.77, and 2.92 for age ≥50 years and 0.73, 0.79, 0.67, 0.73, and 2.41 for age <50 years. CONCLUSION: The study models can serve as reliable screening tools to assess the risk of distal radial fracture in the general population before bone mineral density testing. In addition, they can be integrated into decision support systems to help healthcare providers identify high-risk patients for additional evaluation and education, ultimately improving the quality of care.