WeChat Mini Program
Old Version Features

早期肠内营养治疗改善胃癌术后患者临床指标的观察

Yiyao Qianyan(2020)

Cited 0|Views8
Abstract
目的:研究胃癌术后患者采取早期肠内营养治疗对改善临床指标的效果及临床价值.方法:选取我院2019年1—12月提供的50例接受胃癌根治术患者,按照随机数字表法将其分为对照组与观察组,各组25例.对照组在胃癌根治术后接受常规全肠外营养治疗,观察组则在术后接受早期肠内营养治疗.对比两组患者治疗前后营养指标改善情况,术后并发症发生率及胃肠功能恢复情况.结果:手术后观察组血清白蛋白(40.1±7.3)g/L高于对照组血清白蛋白(35.9±6.1)g/L;观察组前白蛋白(120.4±12.3)g/L高于对照组前白蛋白(114.7±10.9)g/L;观察组血红白蛋白(189.7±41.3)g/L高于对照组血红白蛋白(181.9±38.6)g/L,差异具有显著性(P<0.05).手术结束,观察组并发症发生率12.0%低于对照组并发症发生率40.0%,差异具有显著性(P<0.05).术后统计,观察组肠鸣音消失时间(19.2±5.37)h短于对照组肠鸣音消失时间(28.7±6.33)h,观察组术后肛门首次排气时间(72.3±8.75)h短于对照组术后肛门首次排气时间(98.6±9.71)h,差异具有显著性(P<0.05).结论:给予胃癌术后患者早期肠内营养治疗,能够有效增加患者营养指标,缩短胃肠功能恢复时间,同时减少患者术后并发症,值得临床大力借鉴施行.
More
求助PDF
上传PDF
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