trialgpt-matching

name	trialgpt-matching
description	Trial shortlist
keywords	["retrieval","ranking","ClinicalTrials","patient-profile"]
measurable_outcome	Produce ≥5 ranked trials (when available) with rationale + missing-data notes within 3 minutes of receiving a patient query.
license	MIT
metadata	{"author":"TrialGPT Team","version":"1.0.0"}
compatibility	[{"system":"Python 3.9+"}]
allowed-tools	["run_shell_command","read_file"]

TrialGPT Matching

Run the locally checked-out TrialGPT pipeline to retrieve, rank, and explain candidate trials for a patient before deeper eligibility review.

Inputs

• Patient summary (structured JSON or free text) with condition keywords.
• Optional filters: geography, phase, intervention, biomarker.
• Up-to-date ClinicalTrials.gov dump or API access.

Outputs

• Ranked trial table with NCT ID, title, score, and short justification.
• Parsed inclusion/exclusion text ready for downstream eligibility agents.
• Missing data checklist (e.g., "ECOG not provided").

Workflow

1. Setup: cd repo && pip install -r requirements.txt (or reuse env).
2. Trial retrieval: Run TrialGPT retriever to pull candidate trials for the indication.
3. Criteria parsing: Convert eligibility blocks to structured criteria JSON.
4. Patient profiling: Summarize patient facts (labs, prior therapies, biomarkers).
5. Ranking: Execute TrialGPT ranking script to score each trial and emit explanations.
6. Handoff: Export ranked list + structured criteria for trial-eligibility-agent.

Guardrails

• Refresh ClinicalTrials.gov metadata regularly to avoid stale trials.
• Label scores as AI-generated suggestions pending clinician validation.
• Retain prompt/config metadata for audit trails.

References

• Detailed usage instructions and repo layout live in README.md.
• Coordinate with Skills/Clinical/Trial_Eligibility_Agent for criterion-level review.