Datasets:
trial_id string | site_id string | month int64 | pk_sampling_points int64 | sampling_window_hours int64 | missing_pk_rate float64 | model_spec_error_z float64 | exposure_auc_estimate float64 | exposure_auc_variance float64 | dose_recommendation_shift_pct float64 | decision_error_risk_next_90d int64 | label_decision_error_risk_next_90d int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|
TRIAL_PK01 | S01 | 1 | 8 | 24 | 0.04 | 0.2 | 42.1 | 3.5 | 0 | 0 | 0 |
TRIAL_PK01 | S01 | 3 | 7 | 24 | 0.05 | 0.3 | 41.8 | 3.8 | 0.5 | 0 | 0 |
TRIAL_PK01 | S02 | 6 | 5 | 18 | 0.08 | 0.6 | 39.5 | 4.6 | 2 | 0 | 0 |
TRIAL_PK01 | S02 | 9 | 4 | 12 | 0.12 | 1 | 36.9 | 6.2 | 5.5 | 1 | 1 |
TRIAL_PK01 | S03 | 12 | 3 | 10 | 0.15 | 1.3 | 34.8 | 7.4 | 8 | 1 | 1 |
TRIAL_PK02 | S01 | 2 | 8 | 24 | 0.03 | 0.2 | 42.5 | 3.3 | 0 | 0 | 0 |
TRIAL_PK02 | S02 | 5 | 6 | 20 | 0.07 | 0.5 | 40.2 | 4.1 | 1.5 | 0 | 0 |
TRIAL_PK02 | S03 | 8 | 4 | 14 | 0.11 | 0.9 | 37.6 | 5.8 | 4.5 | 1 | 1 |
TRIAL_PK02 | S03 | 11 | 3 | 10 | 0.14 | 1.2 | 35.5 | 7 | 7.2 | 1 | 1 |
TRIAL_PK02 | S04 | 4 | 7 | 24 | 0.05 | 0.3 | 41.6 | 3.7 | 0.8 | 0 | 0 |
Clinical Quad PK Sampling Sparse Data Model Misspecification Dose Recommendation Error v0.1
Each row is a site monthly snapshot.
Core quad
PK sampling density
Sparse data
Model misspecification
Dose recommendation error
Target
label_decision_error_risk_next_90d
Files
data/train.csv
data/tester.csv
scorer.py
Evaluation
Run model on data/tester.csv
Return predictions row aligned
Score with scorer.py
License
MIT
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.
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