Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.

Background

Cementless bipolar hemiarthroplasty appears to be a suitable method for the treatment of intertrochanteric fracture in octogenarians. However, stable fixation of the posteromedial fragment is necessary to avoid stem subsidence.

The volume of hip arthroplasty is stiffly increasing because of excellent clinical outcomes, however it has not been shown to decrease the incidence of transfusions due to bleeding related to this surgery. This is an important consideration since there are concerns about the side effects and social costs of transfusions. First, anemia should be assessed at least 30 days before elective hip arthroplasty, and if the subject is diagnosed as having anemia, an additional examination of the cause of the anemia should be carried and steps taken to address the anemia. Available iron treatments for anemia take 7 to 10 days to facilitate erythropoiesis, and preoperative iron supplementation, either oral or intravenous, is recommended. When using oral supplements for iron storage, administer elemental iron 100 mg daily for 2 to 6 weeks before surgery, and calculate the dose using intravenous supplement. Tranexamic acid (TXA) is a synthetic derivative of the lysine component, which reduces blood loss by inhibiting fibrinolysis and clot degradation. TXA is known to be an effective agent for reducing postoperative bleeding and reducing the need for transfusions in primary and revision total hip arthroplasties. Patient blood management has improved the clinical outcome after hip arthroplasty through the introduction and research of various agents, thereby reducing the need for allogeneic blood transfusions and reducing the risk of transfusion-related infections and the duration of hospitalizations.

In the medical field, various studies using artificial intelligence (AI) techniques have been attempted. Numerous attempts have been made to diagnose and classify diseases using image data. However, different forms of fracture exist, and inaccurate results have been confirmed depending on condition at the time of imaging, which is problematic. To overcome this limitation, we present an encoderdecoder structured neural network that utilizes radiology reports as ancillary information at training. This is a type of meta-learning method used to generate sufficiently adequate features for classification. The proposed model learns representation for classification from X-ray images and radiology reports simultaneously. When using a dataset of only 459 cases for algorithm training, the model achieved a favorable performance in a test dataset containing 227 cases (classification accuracy of 86.78% and classification F1 score of 0.867 for fracture or normal classification). This finding demonstrates the potential for deep learning to improve performance and accelerate application of AI in clinical practice. In general, X-ray images and radiology reports offer complementary information to a physician who wants to make an informed decision. In the classical diagnosis process, the radiologist reads the image and notes the findings, and then the physician makes a corresponding diagnosis and appropriate decision. However, due to the success of deep learning, recent attempts to achieve a high-performance classifier with a deep neural network (DNN) that only inputs images have increased. Since GoogLeNet outperformed humans in 2014 1 , efforts to develop a high-performance classifier in various areas have continued. The deep learning method is currently popular; however, application to medical fields remains challenging. In particular, protection of patient medical information and unwillingness to share information between hospitals causes difficulty in acquiring a sufficient number of medical images to adequately train DNNs. This leads to performance degradation of DNNs with a relatively large number of parameters, thus requiring a more sophisticated learning algorithm. In addition, the numerous parameters require tuning based on physician assumptions and experience against concrete problems and training datasets, a tedious and resource-intensive task. Meta-learning is a recent technique to overcome (i.e., automate) this problem. The task is also known as "learning to learn" and aims to design models that can learn new tasks rapidly. Several studies have been proposed to apply metalearning techniques to medical images 2,3. Kim et al. 2 used few-shot learning, which is a type of meta-learning method for early diagnosis of glaucoma in fundus images. The authors developed a predictive model based on matching neural network architecture 4 , and showed that the model obtained greater effectiveness than vanilla DNNs. Maicas et al. 3 presented a simple experiment to demonstrate use of meta-learning for fine-tuning a medical image datas...