CaseReportCollective: A Large-Scale LLM-Extracted Dataset for Structured Medical Case Reports

Case reports provide critical insights into rare and atypical diseases, but extracting structured knowledge remains challenging due to unstructured text and domain-specific terminology.We introduce CaseReportCollective, an LLM-extracted dataset of 85,961 openaccess case reports spanning 37 years, validated through programmatic and human evaluation.Our dataset reveals key publication and demographic trends, including a significant increase in open-access case reports over the past decade, shifts in focus from oncology to COVID-19 and sex disparities in reporting across different medical conditions.Using CaseReportCollective, we further explore embedding-based retrieval for similar medical topics through accumulated similarity scores across extracted structured information.We also conducted detailed error analyses on the retrieval ranking, finding that highly reported topics dominate retrieval and the retrieval is driven by lexical overlap rather than underlying clinical relevance, often failing to distinguish between semantically similar yet mechanistically distinct conditions.Future work should focus on clinically aware embeddings adjusted for long-tailed case distributions to improve retrieval accuracy.