ClarusC64/clinical-quad-safety-signal-latency-reporting-lag-conmed-confound-v0.1
Clarus Clinical Quad Coupling Safety Signal Latency Reporting Lag Conmed Confound v0.1 What this dataset isThis dataset tests whether a model can detect latent safety signals when four interacting nodes create uncertainty. Quad coupling nodes Emerging safety event pattern Reporting or entry latency Concomitant medication or behavior confound Governance decision timing such as DSMB, batch release, or safety review Input One vignette OutputReturn strict JSON only. Required output… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-safety-signal-latency-reporting-lag-conmed-confound-v0.1.
Clarus Clinical Quad Coupling Safety Signal Latency Reporting Lag Conmed Confound v0.1
What this dataset is This dataset tests whether a model can detect latent safety signals when four interacting nodes create uncertainty.
Quad coupling nodes
- Emerging safety event pattern
- Reporting or entry latency
- Concomitant medication or behavior confound
- Governance decision timing such as DSMB, batch release, or safety review
Input
- One vignette
Output Return strict JSON only.
Required output JSON keys
- safetysignalrisk
- risk_type
- driver_nodes
- recommended_action
- action_detail
- rationale
- confidence
Files
- data/train.csv
- data/test.csv
- scorer.py
Scoring
- Required key presence
- Risk classification
- Risk type match
- Driver node overlap
- Recommended action match
- Action detail completeness
- Rationale length
- Confidence within 0 to 1
Run scoring Create JSONL {"id":"SS-T01","output":"{...your json...}"}
Run python scorer.py --goldcsv data/test.csv --predsjsonl your_outputs.jsonl