Efficacy of an Omaha system-based remote ergonomic intervention program on self-reported work-related musculoskeletal disorders (WMSDs) - A randomized controlled study.

Purpose: Heavy biomechanical loadings at workplaces may lead to high risks of work-related musculoskeletal disorders. This study aimed to explore the efficacy of an Omaha System-based remote ergonomic intervention program on self-reported work-related musculoskeletal disorders among frontline nurses. Materials and methods: From July to October 2020, 94 nurses with self-reported pain in one of the three body parts, i.e., neck, shoulder, and low back, were selected and were randomly divided into two groups. The intervention group received a newly developed remote program, where the control group received general information and guidance on health and life. Program outcome was evaluated by a quick exposure check approach. Results: After 6 weeks, the intervention group exhibited significantly less stress in the low back, neck, and shoulder/forearms, compared to the control group (p < 0.05). In addition, the occurrence of awkward postures, such as extreme trunk flexion or twisting, was also significantly reduced (p < 0.05). Conclusions: The newly developed Omaha System-based remote intervention program may be a valid alternative to traditional programs for frontline nurses during the COVID-19 pandemic, reducing biomechanical loadings and awkward postures during daily nursing operations.

A novel hybrid framework based on temporal convolution network and transformer for network traffic prediction.

BACKGROUND: Accurately predicting mobile network traffic can help mobile network operators allocate resources more rationally and can facilitate stable and fast network services to users. However, due to burstiness and uncertainty, it is difficult to accurately predict network traffic. METHODOLOGY: Considering the spatio-temporal correlation of network traffic, we proposed a deep-learning model, Convolutional Block Attention Module (CBAM) Spatio-Temporal Convolution Network-Transformer, for time-series prediction based on a CBAM attention mechanism, a Temporal Convolutional Network (TCN), and Transformer with a sparse self-attention mechanism. The model can be used to extract the spatio-temporal features of network traffic for prediction. First, we used the improved TCN for spatial information and added the CBAM attention mechanism, which we named CSTCN. This model dealt with important temporal and spatial features in network traffic. Second, Transformer was used to extract spatio-temporal features based on the sparse self-attention mechanism. The experiments in comparison with the baseline showed that the above work helped significantly to improve the prediction accuracy. We conducted experiments on a real network traffic dataset in the city of Milan. RESULTS: The results showed that CSTCN-Transformer reduced the mean square error and the mean average error of prediction results by 65.16%, 64.97%, and 60.26%, and by 51.36%, 53.10%, and 38.24%, respectively, compared to CSTCN, a Long Short-Term Memory network, and Transformer on test sets, which justified the model design in this paper.

A minimally invasive technique for decompression of Chiari malformation type I (DECMI study): study protocol for a randomised controlled trial.

INTRODUCTION: Chiari malformation type I (CM-I) is a congenital hindbrain anomaly that requires surgical decompression in symptomatic patients. Posterior fossa decompression with duraplasty (PFDD) has been widely practiced in Chiari decompression, but dural opening carries a high risk of surgical complications. A minimally invasive technique, dural splitting decompression (DSD), preserves the inner layer of the dura without dural opening and duraplasty, potentially reducing surgical complications, length of operative time and hospital stay, and cost. If DSD is non-inferior to PFDD in terms of clinical improvement, DSD could be an alternative treatment modality for CM-I. So far, no randomised study of surgical treatment of CM-I has been reported. This study aims to evaluate if DSD is an effective, safe and cost-saving treatment modality for adult CM-I patients, and may provide evidence for using the minimally invasive procedure extensively. METHODS AND ANALYSIS: DECMI is a randomised controlled, single-masked, non-inferiority, single centre clinical trial. Participants meeting the criteria will be randomised to the DSD group and the PFDD group in a 1:1 ratio. The primary outcome is the rate of clinical improvement, which is defined as the complete resolution or partial improvement of the presenting symptoms/signs. The secondary outcomes consist of the incidence of syrinx reduction, postoperative morbidity rates, reoperation rate, quality of life (QoL) and healthcare resource utilisation. A total of 160 patients will be included and followed up at 3 and 12 months postoperatively. ETHICS AND DISSEMINATION: The study protocol was approved by the Biological and Medical Ethics Committee of West China Hospital. The findings of this trial will be published in a peer-reviewed scientific journal and presented at scientific conferences. TRIAL REGISTRATION NUMBER: ChiCTR-TRC-14004099.