Hypertonic Solution As an Optimal Submucosal Injection Solution for Endoscopic Resection of Gastrointestinal Mucosal Lesions: Systematic Review and Network Meta-Analysis
DIGESTIVE ENDOSCOPY(2024)
Fourth Mil Med Univ | Air Force Medical University
Abstract
Objectives Based on different physicochemical properties, common submucosal injection solutions could be classified into three categories: normal saline solution (NS), hypertonic solution (HS), and viscous solution (VS). We compared the efficacy and safety of various categories of solutions in this network meta‐analysis of randomized controlled trials (RCTs) to identify the optimal submucosal injection fluid. Methods PubMed, Embase, Web of Science, and the Cochrane Library were searched for RCTs that compared the efficacy and safety of NS, HS, and VS during endoscopic resection for gastrointestinal (GI) mucosal lesions. Pairwise and network analyses were conducted to determine the ranking of different fluids. Results Thirteen RCTs were included in the final analysis with 1637 patients (1639 lesions). HS outperformed NS in rates of en bloc (pooled relative risk [RR] 1.50; 95% confidence interval [CI] 1.10–1.90), overall bleeding (pooled odds ratio [OR] 0.33; 95% CI 0.10–0.88; lesions >10 mm OR 4.65 × 10 −2 ; 95% CI 1.10 × 10 −3 –0.46), and intraoperative bleeding (lesions >10 mm OR 7.10 × 10 −6 ; 95% CI 4.30 × 10 −18 –0.26). HS showed the highest probability of ranking first in each outcome except for the volume of injection. Although VS was superior to NS in rates of en bloc, overall, and intraoperative bleeding in the lesions >10 mm subgroup, and required less fluid in pooled analysis, it ranked last in cost of submucosal injection solution. Conclusions Both HS and VS were superior to NS in comparisons of efficacy and safety. Considering the better performance and potentially low cost, HS might be an optimal choice during gastrointestinal endoscopic resection, especially for colorectal endoscopic mucosal resection.
MoreTranslated text
Key words
endoscopic mucosal resection,endoscopic resection,endoscopic submucosal dissection,gastrointestinal mucosal lesion,submucosal injection solution
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
Related Papers
2000
被引用173 | 浏览
2005
被引用66 | 浏览
2013
被引用32 | 浏览
2010
被引用147 | 浏览
2004
被引用313 | 浏览
2012
被引用55 | 浏览
2012
被引用34 | 浏览
2014
被引用40 | 浏览
2017
被引用45 | 浏览
2018
被引用52 | 浏览
2020
被引用5 | 浏览
2021
被引用2111 | 浏览
2021
被引用5 | 浏览
2022
被引用7 | 浏览
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