Intra-biliary hydatid cyst rupture: A rare case report with superinfection

Key Clinical Message

Hydatid cysts, primarily found in the liver (70%), are caused by parasitic infections and can lead to severe complications such as cyst rupture. This case report describes a unique instance of a hydatid liver cyst occupying the right lobe with a communicating part with the biliary tree that ruptured showing a concurrent superinfection.

Abstract

Hydatid cysts are a clinical pathology resulting from parasitic infections. They may occur in different organs of the body. However, these are mostly found in the liver (70%). This can cause significant complications including cyst rupture. Several case reports have described various hydatid cyst ruptures; however, only a few have reported an intra-biliary hydatid cyst rupture. A 24-year-old male patient presented with right upper quadrant pain, jaundice, dark urine, and pale stool. Imaging studies, including Magnetic resonance cholangiopancreatography (MRCP) and computed tomography (CT), revealed a beavertail liver, cystobiliary communication and intrahepatic biliary tree-ruptured hydatid cysts. The cyst was in the right liver lobe, which is the most common site for hydatid cysts. Surgical intervention involving laparoscopic de-roofing and cyst removal resulted in a smooth recovery without complications. Several case reports have described various hydatid cyst ruptures; however, only a few have reported originally placed intra-biliary hydatid cyst ruptures. This case report describes a unique instance of a hydatid liver cyst occupying the right lobe with a communicating part with the biliary tree that ruptured showing a concurrent superinfection.

Streamlining Systematic Reviews: Harnessing Large Language Models for Quality Assessment and Risk-of-Bias Evaluation

This editorial explores the innovative application of large language Models (LLMs) in conducting systematic reviews, specifically focusing on quality assessment and risk-of-bias evaluation. As integral components of systematic reviews, these tasks traditionally require extensive human effort, subjectivity, and time. Integrating LLMs can revolutionize this process, providing an objective, consistent, and rapid methodology for quality assessment and risk-of-bias evaluation. With their ability to comprehend context, predict semantic relationships, and extract relevant information, LLMs can effectively appraise study quality and risk of bias. However, careful consideration must be given to potential risks and limitations associated with over-reliance on machine learning models and inherent biases in training data. An optimal balance and combination between human expertise and automated LLM evaluation might offer the most effective approach to advance and streamline the field of evidence synthesis.