What Actions Can Be Used to Prevent Peripheral Nerve Injury?
Evidence-Based Practice of Anesthesiology(2023)
Abstract
Perioperative peripheral nerve injury is a significant source of morbidity for patients and a frequent cause of professional liability for anesthesiologists. The etiology of perioperative nerve injury is largely unknown and multifactorial, and insults interact with the patient’s underlying neuronal reserve. There are few prospective studies on the genesis or prevention of perioperative neuropathy. This chapter summarizes the evidence related to the etiology and incidence of perioperative upper and lower extremity peripheral nerve injury. It also summarizes evidence related to nerve injury after peripheral nerve blockade. Because of the absence of randomized controlled trials and a paucity of epidemiologic studies, the evidence on which practice patterns for prevention of perioperative peripheral neuropathy are based is largely consensus opinion. The chapter summarizes the recommendations from the 2018 American Society of Anesthesiologists (ASA) Task Force on Prevention of Perioperative Peripheral Neuropathies concerning perioperative positioning of the patient, use of protective padding, and avoidance of contact with hard surfaces or supports to reduce perioperative neuropathies.
MoreTranslated text
Key words
Peripheral Nerve Surgery,Nerve Injury,Postoperative Pain
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