ClarusC64/regulatory-clinical-translation-integrity-v0.1

What this dataset tests

Meaning must survive translation.

Regulatory language has limits.

Clinical claims must respect them.

Why it exists

Semantic drift happens at translation boundaries.

Conditional becomes absolute.

Surrogate becomes outcome.

This set detects meaning distortion.

Data format

Each row contains

• —regulatory_language
• —clinical_evidence
• —translated_claim
• —translation_pressure
• —constraints
• —failuremodesto_avoid
• —target_behaviors
• —gold_checklist

Feed the model

• —regulatory_language
• —clinical_evidence
• —translated_claim

Score for

• —preservation of modality and conditions
• —avoidance of semantic inflation
• —explicit mismatch detection
• —corrective guidance

Translation pressures

• —scope_generalization
• —endpoint_upgrade
• —authorization_inflation
• —modalverbloss
• —biomarkertoclinical
• —role_shift
• —caution_removal
• —population_drift
• —contingency_erasure
• —design_inversion
• —selection_loss
• —signal_inflation

Questions you must answer

• —What did the regulator actually say
• —What did the evidence actually show
• —Where did meaning inflate
• —How should language be corrected

Suggested prompt wrapper

System

You evaluate integrity of translation between regulatory language and clinical claims.

User

Regulatory Language {regulatory_language}

Clinical Evidence {clinical_evidence}

Translated Claim {translated_claim}

Scoring

Use scorer.py.

It returns

• —score from 0 to 1
• —semantic integrity signals

Known failure signatures

• —Conditional approvals treated as definitive
• —Surrogate endpoints reframed as outcomes
• —Accelerated approvals presented as standards
• —Population gates removed

Citation

ClarusC64 dataset family