WeChat Mini Program
Old Version Features

    • 膝骨关节炎患者髋关节膝关节运动特征分析

    马淑敏,高谦,林瑞珠,谢娜, 徐峰, 陈小云

    Shanxi Medical Journal（2023）

    Cited 0|Views4
    Abstract
    目的 通过步态分析探讨中老年膝骨关节炎(knee osteoarthritis,KOA)患者步态中膝关节以及髋关节的三维运动特征,为KOA患者临床康复训练提供依据.方法 纳入单侧中老年KOA患者 21 例为观察组,20 例健康中老年人为对照组.2 组的年龄、身高、体质量和体质指数(BMI)相匹配.2 组受试者均使用Gaitboter步态分析系统进行步态测试,对比正常行走时 2 组受试者步行过程中支撑期与摆动期髋、膝关节三维运动特征差异,包括屈曲、伸展,外展、内收及外旋、内旋活动度.结果 KOA组支撑相膝关节屈曲和内收角度较对照组下降(P＜0.05),摆动相膝关节屈曲和外旋角度较对照组下降(P＜0.05).KOA组支撑相髋关节外展和内外旋角度较对照组下降(P＜0.05),摆动相髋关节外旋角度较对照组下降(P＜0.05).结论 中老年KOA患者步行过程中膝关节活动范围较正常人下降,膝关节屈曲、外旋角度减少,同时髋关节外展、外旋活动范围也减少.提示在KOA患者的临床康复训练中,除了常规注重膝关节外,也要重视髋关节功能状态的改善.
    More
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined