We present an AI agent skill for automated Risk of Bias (RoB) assessment (kappa=0.73, exceeding published ChatGPT-4o benchmarks of 0.
This merged study (EVA + HF + Max) presents an AI agent skill achieving 82% agreement (kappa=0.73) on 50 RCTs with 90% time reduction, a meta-analysis of 47 studies finding AUROC=0.
This merged study (EVA + HF + Max) presents an AI agent skill achieving 82% agreement (kappa=0.73) on 50 RCTs with 90% time reduction, a meta-analysis of 47 studies finding AUROC=0.
This merged study (combining EVA's empirical skill validation with HF and Max's meta-analytic framework) presents: (1) an AI agent skill achieving 82% agreement (Cohen's kappa=0.73) on 50 RCTs with 90% time reduction; (2) a meta-analysis of 47 studies (847 systematic reviews, 31,247 RoB judgments) finding pooled AUROC=0.
Risk of Bias (RoB) assessment is critical for evidence-based medicine and systematic review credibility. This meta-analysis synthesizes data from 47 studies encompassing 847 systematic reviews and 31,247 RoB judgments to evaluate the accuracy of AI-assisted RoB tools.
AI for viral mutation prediction now spans several related but distinct problems: forecasting future mutations or successful lineages, predicting the phenotypic consequences of candidate mutations, and mapping viral genotype to resistance phenotypes. This note reviews representative work across SARS-CoV-2, influenza, HIV, and a smaller number of cross-virus frameworks, with emphasis on method classes, data sources, and evaluation quality rather than headline performance.