Postoperative awake prone position in geriatric patients with hip fractures: a protocol for a randomized controlled trial on the efficacy of postoperative prone position in reducing pulmonary complications and improving oxygenation.

INTRODUCTION: Postoperative pulmonary complications (PPCs) are prevalent in geriatric patients with hip fractures. Low oxygen level is one of the most important risk factors for PPCs. Prone position has been proven efficacy in improving oxygenation and delaying the progress of pulmonary diseases, especially in patients with acute respiratory distress syndrome induced by multiple etiologies. The application of awake prone position (APP) has also attracted widespread attention in recent years. A randomized controlled trial (RCT) will be carried out to measure the effect of postoperative APP in a population of geriatric patients undergoing hip fracture surgery. METHODS: This is an RCT. Patients older than 65 years old admitted through the emergency department and diagnosed with an intertrochanteric or femoral neck fracture will be eligible for enrollment and assigned randomly to the control group with routine postoperative management of orthopedics or APP group with an additional prone position for the first three consecutive postoperative days (PODs). Patients receiving conservative treatment will not be eligible for enrollment. We will record the difference in the patient's room-air-breathing arterial partial pressure of oxygen (PaO2) values between the 4th POD (POD 4) and emergency visits, the morbidity of PPCs and other postoperative complications, and length of stay. The incidence of PPCs, readmission rates, and mortality rates will be followed up for 90 PODs. DISCUSSION: We describe the protocol for a single-center RCT that will evaluate the efficacy of postoperative APP treatment in reducing pulmonary complications and improving oxygenation in geriatric patients with hip fractures. ETHICS AND DISSEMINATION: This protocol was approved by the independent ethics committee (IEC) for Clinical Research of Zhongda Hospital, Affiliated to Southeast University, and is registered on the Chinese Clinical Trial Registry. The findings of the trial will be disseminated through peer-reviewed journals. ETHICS APPROVAL NUMBER: 2021ZDSYLL203-P01 TRIAL REGISTRATION: ChiCTR ChiCTR2100049311 . Registered on 29 July 2021. TRIAL STATUS: Recruiting. Recruitment is expected to be completed in December 2024.

Identifying informative tweets during a pandemic via a topic-aware neural language model.

Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.

The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China.

Based on the high-frequency heterogeneous autoregressive (HAR) model, this paper investigates whether coronavirus news (in China and globally) contains incremental information to predict the volatility of China's crude oil, and studies which types of coronavirus news can better forecast China's crude oil volatility. Considering the information overlap among various coronavirus news items and making full use of the information in various coronavirus news items, this paper uses two prevailing shrinkage methods, lasso and elastic nets, to select coronavirus news items and then uses the HAR model to predict China's crude oil volatility. The results show that (i) coronavirus news can be utilized to significantly predict China's crude oil volatility for both in-sample and out-of-sample analyses; (ii) the Panic Index (PI) and the Country Sentiment Index (CSI) have a greater impact on China's crude oil volatility. Additionally, China's Fake News Index (FNI) have a significant impact on China's crude oil volatility forecast; and (iii) global coronavirus news provides more incremental information than China's coronavirus news for predicting the volatility of China's crude oil market, which indicates that global coronavirus news is also a key factor to consider when predicting the market volatility of China's crude oil.