Tag: screening articles
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A Human-LLM Collaborative Annotation Approach for Screening Precision Oncology RCT Articles
Introduction Systematic reviews in precision oncology rely on identifying randomized controlled trials (RCTs) to compare therapies and guide clinical decisions. However, screening thousands of articles to find eligible RCTs is labor-intensive and prone to human error. While supervised learning can accelerate this process, it often demands large labeled datasets and careful tuning, especially for nuanced…
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A human-LLM collaborative annotation approach for screening articles on precision oncology randomized controlled trials
Why a human-LLM collaborative approach matters Systematic reviews in precision oncology require screening thousands of articles to identify randomized controlled trials (RCTs) that illuminate biomarker-driven therapies and targeted interventions. Manual screening, while thorough, is time-consuming and resource-intensive. Large language models (LLMs) can accelerate triage by quickly categorizing relevance and extracting key trial details, but their…