What problem does it solve?
This Skill helps developers and medical researchers turn natural-language clinical questions into structured, ranked evidence packages instead of manually searching and combining multiple literature databases.
Core Features & Use Cases
- EBM Question Classification: Classify treatment, diagnosis, prognosis, etiology, prevention, and economic questions, then extract Population, Intervention, Comparison, and Outcome elements.
- Multi-Source Literature Routing: Coordinate concurrent searches across PubMed, OpenAlex, Semantic Scholar, and the Anta Afu RAG source, with deduplication, enrichment, and four-tier ranking.
- MCP and Systematic Review Support: Work with the ask, search, list, and persist_qa tools while preparing evidence for systematic reviews, meta-analyses, full-text screening, and knowledge-base persistence.
- Use Case: For a question about SGLT2 inhibitors and heart-failure outcomes, route the query, assemble relevant studies, enrich them with citation and MeSH data, and return a ranked evidence package.
Quick Start
Ask the agent to use via54medit to classify a clinical question, extract its PICO elements, search all available literature sources, and return a ranked evidence package.