脑电变化和BASED评分与54例婴儿痉挛症促肾上腺皮质激素疗效的相关性
Journal of Shandong University (Health Science)（2022）
首都医科大学附属北京儿童医院
Abstract
目的 探讨婴儿痉挛症患者促肾上腺皮质激素(ACTH)治疗后脑电图变化特征及其对疗效的评估价值.方法 回顾性分析2019年11月至2021年4月首都医科大学附属北京儿童医院神经内科确诊为婴儿痉挛症并给予ACTH治疗的54例患者临床及脑电图资料并进行随访.脑电图分析内容包括发作间期背景活动异常(主要是慢波)、癫痫样放电(癫痫样波形和高度失律).采用波幅负荷和癫痫样放电评分(BASED)系统进一步评价ACTH治疗前后脑电图变化.根据ACTH治疗后14 d时是否有癫痫发作分为无发作组(n=29例)和发作组(n=25例),对两组治疗前后的EEG进行比较.结果 治疗后无发作组中局灶性慢波(18例)、局灶与多灶性癫痫样放电(28例)明显高于发作组(5例、15例),高度失律(1例)明显少于发作组(10例),两组差异均有统计学意义(P<0.05).无发作组BASED提示治疗后脑电图缓解(26例)明显高于发作组(7例)(P<0.0001),脑电图缓解与临床疗效一致(P<0.001).随访12～29个月,无发作组9例复发,发作组16例仍有痉挛发作,痉挛发作控制率分别为68.97％、36％.结论 发作间期脑电图的特点与临床疗效具有一定相关性,BASED显示ACTH治疗后脑电图缓解时,提示临床疗效好,BASED量化可协助临床的疗效判断.
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