Background: Laparoscopic hernia repair is a minimally invasive surgery, but patients may experience emergence agitation (EA) during the post-anesthesia recovery period, which can increase pain and lead to complications such as wound reopening and bleeding. There is limited research on the risk factors for this agitation, and few effective tools exist to predict it. Therefore, by integrating clinical data, we have developed nomograms and random forest predictive models to help clinicians predict and potentially prevent EA.
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