team holly

Glioma Copilot helps a neuro-oncology clinician and, through them, the patient - decide whether a clinical trial fits this specific glioma patient. It is not a trial finder: clinicians already know the trials. The hard, still-manual part is fit. Eligibility criteria and patient charts are both free text, so after filtering, a clinician verifies every criterion by hand , since the 2021 WHO reclassification, a stale diagnosis label can silently misroute eligibility. I built a copilot that reads both sides and produces a per-criterion fit table , met, not-met, or unknown, each source-cited , turning unknowns into a workup checklist. Its principle: Claude is never the source of truth. Diagnosis runs a deterministic WHO CNS5 rule engine; drugs are grounded in RxNorm/ChEMBL; the literature layer is generated with Claude Science and audited against PubMed (23/23, zero hallucinated). A three-agent loop drafts, an Opus verifier rewrites over-claims, and an independent Opus auditor re-derives eligibility blind and challenges the table. What we found: with real de-identified TCGA patients against real strict trials, almost nothing is a clean "eligible" , so the tool refuses to fake a green match and shows honest unknowns instead. And it does what no trial tool does ,it brings the patient into the decision with a plain-language explanation, a curated FAQ, and a shared-decision handout. It shows the clinician why a trial fits, or doesn't

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