Research on the Current Situation of Emergency Vehicle Management in Sports Medicine and Athletic Hospitals

Objective: To investigate the current state of emergency vehicle management within sports medicine and athletic hospitals, with a focus on hospitals located in Zhejiang Province. This study aims to provide valuable insights and recommendations for enhancing the management of emergency vehicles in the context of sports medicine. Methods: A convenience sampling approach was employed, involving surveys conducted with nurses from a total of 40 sports medicine and athletic hospitals located in 15 cities across Zhejiang Province during the period from April to May 2022. Results: The findings obtained through the questionnaire survey revealed noteworthy aspects. Specifically, 15.89% of the surveyed hospital departments lacked a dedicated pharmacist responsible for regular quality checks of emergency drugs. Furthermore, 55.14% of the respondents expressed concerns about the athlete nurses' level of knowledge regarding rescue drugs and related items. Alarmingly, the study found that 100% of the departments relied solely on manual inventory management for emergency drugs and items, with only 39.39% of them implementing partial information management systems, leading to varying degrees of inventory discrepancies. Conclusion: The study highlights certain shortcomings in the supervision and management systems related to emergency medicines and equipment within sports medicine and athletic hospitals. Additionally, personnel management practices appear to be lacking in some athletic hospitals. (AU)

ConvWin-UNet: UNet-like hierarchical vision Transformer combined with convolution for medical image segmentation.

Convolutional Neural Network (CNN) plays a vital role in the development of computer vision applications. The depth neural network composed of U-shaped structures and jump connections is widely used in various medical image tasks. Recently, based on the self-attention mechanism, the Transformer structure has made great progress and tends to replace CNN, and it has great advantages in understanding global information. In this paper, the ConvWin Transformer structure is proposed, which refers to the W-MSA structure in Swin and combines with the convolution. It can not only accelerate the convergence speed, but also enrich the information exchange between patches and improve the understanding of local information. Then, it is integrated with UNet, a U-shaped architecture commonly used in medical image segmentation, to form a structure called ConvWin-UNet. Meanwhile, this paper improves the patch expanding layer to perform the upsampling operation. The experimental results on the Hubmap datasets and synapse multi-organ segmentation dataset indicate that the proposed ConvWin-UNet structure achieves excellent results. Partial code and models of this work are available at https://github.com/xmFeng-hdu/ConvWin-UNet.