ClarusC64/clinical-quad-trial-pop-variance-realworld-subgroup-signal-generalization-claim-drift-v0.1
What this repo does This dataset models population mismatch narrative drift in clinical trial reporting. It predicts when the interaction between trial population variance, real-world variance, subgroup signal strength, and generalization claim intensity indicates that narrative claims extend beyond what the data supports. Core quad trial_population_variance_index real_world_variance_index subgroup_signal_strength_index generalization_claim_index Prediction target label_claim_drift Row… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-trial-pop-variance-realworld-subgroup-signal-generalization-claim-drift-v0.1.
What this repo does
This dataset models population mismatch narrative drift in clinical trial reporting. It predicts when the interaction between trial population variance, real-world variance, subgroup signal strength, and generalization claim intensity indicates that narrative claims extend beyond what the data supports.
Core quad
trialpopulationvarianceindex realworldvarianceindex subgroupsignalstrengthindex generalizationclaim_index
Prediction target
labelclaimdrift
Row structure
Each row represents a trial-to-claim snapshot. The model predicts whether the coupled population and claim conditions produce a claim drift event within the narrative.
Files
data/train.csv data/tester.csv scorer.py
Evaluation
Run predictions on tester.csv Add column prediction Score with scorer.py
License
MIT
Structural Note
This dataset identifies a measurable coupling pattern associated with systemic instability. The sample demonstrates the geometry. Production-scale data determines operational exposure.
What Production Deployment Enables
• 50K–1M row datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support
Small samples reveal structure. Scale reveals consequence.
Enterprise & Research Collaboration
Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.
For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com
Instability is detectable. Governance determines whether it propagates.