ClarusC64/clinical-regulatory-evidence-correspondence-v0.1

What this dataset tests

Regulatory claims must map to evidence scope.

Population boundaries matter.

Why it exists

Regulatory language can drift.

Indications expand.

Subgroups disappear.

This set detects when claims exceed the evidence base.

Data format

Each row contains

• —evidence_base
• —regulatory_claim
• —correspondence_pressure
• —constraints
• —failuremodesto_avoid
• —target_behaviors
• —gold_checklist

Feed the model

• —evidence_base
• —regulatory_claim

Score for

• —explicit correspondence classification
• —mapping to inclusion and exclusion criteria
• —preservation of subgroup gates
• —correction guidance

Correspondence pressures

• —label_expansion
• —agescopecreep
• —stage_shift
• —setting_expansion
• —subtype_drift
• —severity_creep
• —renalstageexpansion
• —pediatric_gap
• —indication_bundling
• —biomarkerscopeloss
• —endpoint_upgrade
• —superiority_overreach
• —lineoftherapy_shift

Questions you must answer

• —Who was studied
• —Who was excluded
• —What endpoint was proven
• —Does the claim exceed scope
• —What wording would be correct

Suggested prompt wrapper

System

You evaluate whether a regulatory claim corresponds to the clinical evidence base.

You must classify the claim as supported, partial, or unsupported.

User

Evidence Base {evidence_base}

Regulatory Claim {regulatory_claim}

Scoring

Use scorer.py.

It returns

• —score from 0 to 1
• —mapping signals

Known failure signatures

• —HF subtype expansion
• —Line-of-therapy drift
• —Biomarker gating removed
• —Surrogate upgraded to clinical endpoint

Citation

ClarusC64 dataset family.