# Nick Lamb

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Nick Lamb](https://cerebralvalley.ai/u/pharmatools)
- **GitHub:** https://github.com/nickjlamb/studydiff  https://studydiff.pharmatools.ai https://www.npmjs.com/package/studydiff-mcp
- **Demo video:** https://youtu.be/BwegvGp51-4
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/16

When two well-run papers disagree, it is rarely because one of them is wrong. Usually the reason is a methodological difference buried in the methods. StudyDiff is a contradiction explorer for bench scientists: give it two papers and it explains why they reach different conclusions, grounding every claim in the source text.

Claude (claude-sonnet-5), through forced tool-use, turns each paper into a fixed study-card schema, returning a verbatim supporting quote for every field. A deterministic OpenGATE grounding check then verifies it: every quote must be a real substring of the source, and every number must trace back. There is no LLM-as-judge. Ungrounded fields are downgraded to "not reported" before they can be cited. StudyDiff compares only the verified evidence, ranks the design dimensions that most plausibly drive the disagreement, and suggests what evidence would resolve it. It also ships as an MCP server, so Claude can call the engine as a tool with the same guarantees.

It never invents a confidence score and never picks a winner. If a paper doesn't report something, it says so. Most literature tools help you read a paper. StudyDiff explains why two papers conflict: an explanation, not a summary.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/16. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt