Value of narcotrend anesthesia depth monitoring in predicting POCD in gastrointestinal tumor anesthesia block patients

NT monitoring of anesthetic depth has a less physical impact on patients with gastrointestinal tumor anesthetic block, reduces the degree of postoperative POCD, and has significant clinical value.

Authors

Institutions

Publication Details

Journal
BMC Anesthesiology
Published
2024-10-14
DOI
https://doi.org/10.1186/s12871-024-02762-9
Citations
4
Primary Topic
Intensive Care Unit Cognitive Disorders
Type
article
Field-Weighted Citation Impact
1.69

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Value of narcotrend anesthesia depth monitoring in predicting POCD in gastrointestinal tumor anesthesia block patients

Jianghong Liu, Xueli Zhao, Nie Hong-feng, Zhixun Hu et al.
4 citations
BMC Anesthesiology
Intensive Care Unit Cognitive Disorders
1.69
article

Value of narcotrend anesthesia depth monitoring in predicting POCD in gastrointestinal tumor anesthesia block patients

Jianghong Liu, Xueli Zhao, Nie Hong-feng, Zhixun Hu, Ruina Guo, Xizhong Ma
article en
4 citations

Abstract

NT monitoring of anesthetic depth has a less physical impact on patients with gastrointestinal tumor anesthetic block, reduces the degree of postoperative POCD, and has significant clinical value.

BMC AnesthesiologyVol. 24(1)
Hebei Medical University (CN), Xingtai People's Hospital (CN)
Clinical Trial Center, China Medical University Hospital
Good health and well-being
Openalex Percentile: Top 16%
Intensive Care Unit Cognitive Disorders
1.69
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.