CIViC Evidence

CIViC Evidence focuses on classifying and extracting structured information from scientific literature, particularly regarding the relationships between genomic variants, cancer types, and treatment outcomes. Current research employs machine learning models, including transformer-based architectures like BERT and RoBERTa, and increasingly leverages large language models (LLMs) for tasks such as automated labeling and information extraction from clinical trial reports. This work aims to improve the efficiency and accuracy of evidence synthesis, facilitating more effective decision-making in healthcare and other fields requiring rigorous analysis of complex datasets.