Comparison of Sociodemographic and Radiographic Features in Distal Radio Fracture Treatment: Hand Surgeons versus Non-specialists.
Rafael Bulyk VeigaRenê HobiRicardo Pereira MarotGustavo Zeni SchuroffRoberto Luiz SobaniaIvan Killing KuhnAna Lúcia Campos FaccioniPublished in: Revista brasileira de ortopedia (2024)
Objective: This study evaluated sociodemographic and radiographic features of patients with distal radial fractures treated at a trauma hospital in southern Brazil, comparing those treated by hand surgery specialists (group 1) and non-specialists (group 2). Methods: This study consists of a retrospective cohort of 200 patients treated in 2020. After reviewing medical records and radiographs, the following parameters were analyzed: age, gender, trauma mechanism, laterality, associated comorbidities and fractures, fracture classification (AO), radial height, radial inclination, and volar inclination. Comparison of the two groups used the Student t-test, chi-square test, or Fisher exact test. Results: Most subjects were women (54%), sustained low-energy traumas (58%), and were left-handed (53%). Group 1 had a lower mean age (50.2 years); most of their subjects sustained high-energy trauma (54%) and had type C fractures (73%); type A fractures prevailed in group 2 (72%). Radiographs showed a significant difference regarding the mean radial inclination (21.5° in group 1 and 16.5° in group 2 [ p < 0.001] in women, and 21.3° in group 1 and 17° in group 2 [ p < 0.001] in men) and volar inclination (10.1° and 12.8° in groups 1 and 2, respectively [ p < 0.001]). In addition, the absolute number of cases with reestablished anatomical parameters per the three evaluated variables was also significantly different; all parameters were better in group 1. Conclusion: Hand surgeons treated the most severe fractures and had the best radiographic outcomes.
Keyphrases
- healthcare
- minimally invasive
- machine learning
- emergency department
- body mass index
- type diabetes
- mental health
- pregnant women
- quality improvement
- deep learning
- percutaneous coronary intervention
- early onset
- metabolic syndrome
- weight loss
- skeletal muscle
- density functional theory
- trauma patients
- coronary artery bypass
- newly diagnosed
- drug induced
- electronic health record