drug-discovery-prompts/upstream/mims-harvard-ToolUniverse/skills/tooluniverse-clinical-trial-matching/SCORING_CRITERIA.md

title, task, lineage_type, upstream_source, upstream_sha, imported_at, prompt_class, upstream_changes, author, validated

title	task	lineage_type	upstream_source	upstream_sha	imported_at	prompt_class	upstream_changes	author	validated
Scoring Criteria & Recommendation Tiers		import	https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-clinical-trial-matching/SCORING_CRITERIA.md	e2520a96	2026-06-26	prompt	accepted	upstream	false

Scoring Criteria & Recommendation Tiers

Trial Match Score Components (Total: 0-100)

Molecular Match (0-40 points)

Criterion	Points	Description
Exact biomarker match	40	Trial requires patient's specific variant
Gene-level match	30	Trial requires gene mutation, patient has specific variant
Pathway match	20	Trial targets same pathway as patient's biomarker
No molecular criteria	10	General disease trial
Excluded biomarker	0	Patient's biomarker is in exclusion criteria

Clinical Eligibility (0-25 points)

Criterion	Points	Description
All criteria met	25	Disease, stage, prior treatment all match
Most criteria met	18	1-2 criteria unclear
Some criteria met	10	Several criteria unclear
Clearly ineligible	0	Fails major criterion

Evidence Strength (0-20 points)

Criterion	Points	Description
FDA-approved combination	20	T1 evidence
Phase III positive	15	T2 evidence
Phase II promising	10	T3 evidence
Phase I or no results	5	T4 evidence

Trial Phase (0-10 points)

Phase	Points
Phase III	10
Phase II	8
Phase I/II	6
Phase I	4

Geographic Feasibility (0-5 points)

Criterion	Points
Patient's city/state	5
Same country	3
International only	1
Unknown	0

Evidence Tier Classification

Tier	Symbol	Criteria	Score Impact
T1	[T1]	FDA-approved biomarker-drug, NCCN guideline	20 points
T2	[T2]	Phase III positive, clinical evidence	15 points
T3	[T3]	Phase I/II results, preclinical	10 points
T4	[T4]	Computational, mechanism inference	5 points

Recommendation Tiers

Score	Tier	Label	Action
80-100	Tier 1	Optimal Match	Strongly recommend - contact site immediately
60-79	Tier 2	Good Match	Recommend - discuss with care team
40-59	Tier 3	Possible Match	Consider - needs further eligibility review
0-39	Tier 4	Exploratory	Backup option - consider if Tier 1-3 unavailable

Molecular Match Scoring Logic

def score_molecular_match(patient_biomarkers, trial_requirements):
    """Score molecular match between patient and trial (0-40 points)."""
    if not trial_requirements['required_biomarkers'] and not trial_requirements['excluded_biomarkers']:
        return 10, 'No specific molecular criteria (general trial)'

    patient_genes = {b['gene'].upper() for b in patient_biomarkers}
    required_genes = {b['gene'].upper() for b in trial_requirements['required_biomarkers']}
    excluded_genes = {b['gene'].upper() for b in trial_requirements['excluded_biomarkers']}

    # Check exclusions first
    excluded_match = patient_genes & excluded_genes
    if excluded_match:
        return 0, f'Patient biomarker(s) {excluded_match} are in exclusion criteria'

    if not required_genes:
        return 10, 'No specific biomarker requirements found'

    # Check for exact gene match
    matched_genes = patient_genes & required_genes
    if matched_genes:
        exact_variant_match = False
        for req in trial_requirements['required_biomarkers']:
            for pb in patient_biomarkers:
                if pb['gene'].upper() == req['gene'].upper():
                    alt = pb.get('alteration', '').upper()
                    if alt and alt in req.get('context', '').upper():
                        exact_variant_match = True
                        break

        if exact_variant_match:
            return 40, f'Exact biomarker match: {matched_genes} with specific variant'
        else:
            return 30, f'Gene-level match: {matched_genes} (specific variant match unclear)'

    return 5, 'No direct biomarker match found'

Drug-Biomarker Alignment Scoring

def score_drug_biomarker_alignment(patient_gene_symbols, drug_mechanisms):
    """Check if trial drug targets patient's biomarkers."""
    patient_genes_upper = {g.upper() for g in patient_gene_symbols}

    for mech in drug_mechanisms:
        target_genes = {g.upper() for g in mech.get('target_genes', [])}
        if patient_genes_upper & target_genes:
            return True, f"Drug targets {patient_genes_upper & target_genes} via {mech.get('mechanism')}"

    return False, "No direct target overlap with patient biomarkers"