TrialGPT

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---
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:
  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.

## Core Capabilities
- Operationalize neuro-symbolic multi-agent oncology trial matching by using an oncology-specific knowledge graph, performing criterion-level eligibility reasoning, tracking prospective cohort evaluation results, preserving audit trails, and routing ambiguous inclusion or exclusion criteria to human review.
- Apply the 2026 prospective oncology trial matching pattern by grounding recommendations in an oncology-specific knowledge graph, separating agent roles for eligibility parsing and synthesis, preserving criterion-level evidence, evaluating behavior across prospective patient cohorts, and requiring human review for ambiguous inclusion/exclusion criteria.
- Apply neuro-symbolic multi-agent oncology trial matching with ontology/knowledge-graph grounding, criterion-level reasoning, eligibility conflict resolution, prospective patient-scale evaluation tracking, and human review workflows for ambiguous or high-impact matches.
- Support neuro-symbolic multi-agent oncology trial matching patterns by grounding matches in an oncology-specific knowledge graph, applying criterion-level symbolic checks, assigning multi-agent review roles, prospectively evaluating matching behavior against patient cohorts, and emitting auditable MET/NOT/UNKNOWN rationales while escalating ambiguous inclusion/exclusion criteria for expert review.
- Incorporate patterns from the 2026 prospective 3,804-patient oncology trial matching evaluation: parse eligibility with knowledge-graph support, retain criterion-level reasoning and evidence, track prospective evaluation metrics at patient scale, preserve auditable patient-scale decision trails, and route ambiguous criteria through explicit escalation rules.
- Use an oncology-specific knowledge graph in neuro-symbolic multi-agent matching to extract criterion-level evidence, score confidence for eligibility calls, evaluate behavior prospectively across patient cohorts, and send final or uncertain eligibility determinations to human adjudication.
- Coordinate neuro-symbolic oncology matching agents around an oncology-specific knowledge graph, require criterion-level reasoning for each eligibility call, resolve conflicts among agent outputs before ranking, track patient cohort-scale prospective evaluation context, and preserve audit trails for human trial-navigation review.
- For prospective oncology matching, combine neuro-symbolic multi-agent review with an oncology-specific knowledge graph, provide criterion-level explanations and confidence scoring for each match, retain audit trails, and report prospective validation metrics without treating AI output as final eligibility.

## 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.
- https://pubmed.ncbi.nlm.nih.gov/42004487/


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