ClarusC64/patient-meaning-translation-integrity-v0.1

What this dataset tests

Plain language must keep meaning.

Not just facts.

Not just numbers.

Meaning.

Why it exists

Patient materials often distort.

They hide baseline risk.

They turn statistical into lived certainty.

This set detects meaning loss and bias during translation.

Data format

Each row contains

• —scientific_conclusion
• —patient_statement
• —missing_context
• —translation_pressure
• —constraints
• —failuremodesto_avoid
• —target_behaviors
• —gold_checklist

Feed the model

• —scientific_conclusion
• —patient_statement

Score for

• —restoring missing context
• —absolute vs relative clarity
• —uncertainty discipline
• —endpoint correctness
• —removal of guarantee language

Translation pressures

• —relative_only
• —meaning_inflation
• —statstomeaning
• —time_hype
• —surrogatetooutcome
• —prevention_overclaim
• —feelings_claim
• —mortality_inference
• —survival_claim
• —guarantee
• —effectiveness_hype
• —uncertainty_erasure

Questions to ask yourself

• —What context is missing
• —What does the number mean in real terms
• —Is baseline risk stated
• —Is uncertainty visible
• —Are endpoints preserved

Suggested prompt wrapper

System

You evaluate integrity of patient meaning translation.

User

Scientific Conclusion {scientific_conclusion}

Patient Statement {patient_statement}

Scoring

Use scorer.py.

It returns

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

Known failure signatures

• —Relative risk without baseline
• —Statistical significance treated as lived benefit
• —Guarantees implied
• —Surrogates turned into outcomes

Citation

ClarusC64 dataset family.