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ClarusC64/clinical-quad-protocol-deviation-staffing-drift-adjudication-variance-missingness-bias-v0.2

Clinical Quad Protocol Deviation Staffing Drift Adjudication Variance Missingness Bias v0.2 What this dataset does It tests whether a model can detect operational collapse risk in trial conduct. The quad nodes protocol_deviation staffing_drift adjudication_variance missingness_bias Labels coherent stable staffing low drift and low bias operations remain controlled tradeoff mixed strain issues exist but do not meet collapse pattern collapse_risk all level nodes high and… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-protocol-deviation-staffing-drift-adjudication-variance-missingness-bias-v0.2.

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Clinical Quad Protocol Deviation Staffing Drift Adjudication Variance Missingness Bias v0.2

What this dataset does

It tests whether a model can detect operational collapse risk in trial conduct.

The quad nodes

—protocol_deviation
—staffing_drift
—adjudication_variance
—missingness_bias

Labels

coherent

—stable staffing
—low drift and low bias
—operations remain controlled

tradeoff

—mixed strain
—issues exist but do not meet collapse pattern

collapse_risk

—all level nodes high and staffing drift present
—process control fails across the trial pipeline

What changed in v0.2

—Fixed repo name typo from linical to clinical
—Version bumped so scorer updates are visible
—New scorer with validation, confusion, and error sampling
—Added riskscore and rulepred diagnostics

Files

data/train.csv data/test.csv scorer.py

Run scoring

python scorer.py --predscsv predictions.csv --goldcsv data/test.csv