https://www.selleckchem.com/products/U0126.html
Fascial dehiscence following exploratory laparotomy is associated with significant morbidity and increased mortality. Previously published risk prediction models for fascial dehiscence are dated and limit a surgeon's ability to perform reliable risk assessment intraoperatively. We sought to determine if machine learning can predict fascial dehiscence after exploratory laparotomy. A retrospective cohort study was conducted of 93,024 patients undergoing exploratory laparotomy from the 2011-2018 ACS NSQIP data files. Data were divided into t
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