颈椎椎间盘切除融合术后邻近节段退变比较
Modern Instruments & Mediccal Treatment（2019）
Abstract
目的:分析颈椎椎间盘切除融合术后邻近节段退变情况.方法:回顾性分析我院2013年1月至2017年2月收治的63例颈椎退行性病变患者临床资料,手术方式为颈椎椎间盘切除融合术,手术及随访资料均完整,比较手术前及末次随访时患者COC2角(C2下终板与McGregor线夹角)、C2C7角(C7与C2下终板垂线间夹角)、T1S(T1倾斜角)、NT(颈倾角)、TIA(胸廓入口角)、日本矫形外科协会评分(JOA)及邻近节段颈椎间盘退变情况.结果:术后随访21～56个月,平均(30.04±6.73)个月.术前患者JOA评分为(9.32±1.06)分,末次随访时为(14.87±0.65)分,术前与末次随访比较差异有统计学意义(P＜0.05).末次随访时C2C7角、T1S及NT均较手术前改善,差异有统计学意义(P＜0.05).末次随访时12例(19.05％)患者邻近节段发生退变,其中1例2级和1例1级退变为4级,3例1级退变为3级,6例0级退变为2级,1例0级退变为1级,手术前及末次随访时邻近节段退变情况分布比较差异无统计学意义(P＞0.05).3例(4.76％)患者因邻近节段退变神经症状加重,再次手术后神经症状得到改善.结论:颈椎间盘切除融合术治疗颈椎退行性病变神经症状改善效果显著,但术后存在邻近节段椎间盘退变风险,应灵活处置尽量降低退变发生率.
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