手法复位方式配合石膏外固定治疗跟骨骨折的疗效及应用价值
Chinese Community Doctors(2021)
Abstract
目的:研究手法复位+石膏外固定治疗跟骨骨折患者的效果.方法:2018年1月-2020年9月收治跟骨骨折患者120例,随机分为两组,各60例.参照组应用钢板内固定治疗,试验组采用手法复位+石膏外固定治疗,比较两组治疗效果.结果:试验组治疗后白介素-1β(IL-1β)及白介素-6(IL-6)指标水平低于参照组,差异有统计学意义(P<0.05);试验组治疗后跟骨结节关切角(Bohler角)高于参照组,跟骨宽度低于参照组,差异有统计学意义(P<0.05);试验组治疗后足功能系统(Maryland)评分高于参照组、疼痛视觉模拟(VAS)评分低于参照组,差异有统计学意义(P<0.05);试验组治疗总有效率高于参照组,差异有统计学意义(P<0.05).结论:手法复位+石膏外固定治疗跟骨骨折疗效显著.
More求助PDF
上传PDF
View via Publisher
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