I used Claude to match 200 Clinical Trials to 700 PubMed Papers (everyrow.io)

A recent exploration demonstrated how the AI tool Claude Code was utilized to match 200 clinical trials to 700 PubMed papers, achieving impressive results through a multi-phase approach involving TF-IDF pre-filtering and parallel LLM subagents. Claude Code first reduced potential matches through cosine similarity, then identified explicit trial identifiers, ultimately validating matches based on detailed trial attributes. While Claude achieved a precision of 100%—indicating that all reported matches were correct—it fell short on recall, identifying only about 59% of true linkages, primarily due to its conservative filtering strategy. In contrast, the everrow SDK implemented a more efficient merging process as a single function call, orchestrating hundreds of LLM agents to dynamically scale its operation according to the dataset size. This resulted in higher overall accuracy with an F1 score of 87.2% and a performance that improved as data volume increased. The study highlighted that while both systems have unique strengths, the tailored orchestration of everrow outperformed Claude Code when handling larger datasets, effectively addressing complex data operations that require a nuanced understanding of clinical research context. This distinction underscores the importance of specialized tools in tackling large-scale data challenges within the AI/ML community.