ClarusC64/clinical-quad-endpoint-adjudication-bias-missingness-timing-pressure-v0.1

Clarus Clinical Quad Coupling Endpoint Adjudication Bias Missingness Timing Pressure v0.1

What this dataset is This dataset tests whether a model can detect endpoint adjudication bias created by four interacting forces.

Quad coupling nodes

• —Endpoint rate or classification shift
• —Data latency or packet incompleteness
• —Concomitant exposure or contextual missingness
• —Governance pressure such as interim look, submission, earnings, or regulator briefing

Input

• —One vignette

Output Return strict JSON only.

Required output JSON keys

• —adjudicationbiasrisk
• —bias_type
• —driver_nodes
• —recommended_action
• —action_detail
• —rationale
• —confidence

Files

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

Scoring

• —Required key presence
• —Bias classification
• —Bias type match
• —Driver node overlap
• —Recommended action match
• —Action detail completeness
• —Rationale length
• —Confidence within 0 to 1

Run scoring Create JSONL {"id":"EA-T01","output":"{...your json...}"}

Run python scorer.py --goldcsv data/test.csv --predsjsonl your_outputs.jsonl