Datasets:
trial_id string | site_id string | week int64 | supply_on_time_pct float64 | shipment_delay_days int64 | dose_interruptions_per_patient float64 | missed_dose_rate float64 | exposure_gap_days int64 | pk_trough_drop_pct int64 | efficacy_change float64 | efficacy_loss_next_60d int64 | label_efficacy_loss_next_60d int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|
TRIAL_SUP01 | S01 | 4 | 0.96 | 1 | 0.2 | 0.03 | 1 | 5 | 0.12 | 0 | 0 |
TRIAL_SUP01 | S01 | 6 | 0.94 | 2 | 0.3 | 0.04 | 2 | 8 | 0.1 | 0 | 0 |
TRIAL_SUP01 | S02 | 8 | 0.9 | 4 | 0.5 | 0.06 | 3 | 12 | 0.07 | 0 | 0 |
TRIAL_SUP01 | S02 | 10 | 0.84 | 6 | 0.8 | 0.09 | 5 | 18 | 0.03 | 1 | 1 |
TRIAL_SUP01 | S03 | 12 | 0.78 | 8 | 1 | 0.12 | 7 | 24 | -0.02 | 1 | 1 |
TRIAL_SUP02 | S01 | 5 | 0.97 | 1 | 0.2 | 0.02 | 1 | 4 | 0.13 | 0 | 0 |
TRIAL_SUP02 | S02 | 7 | 0.93 | 2 | 0.3 | 0.04 | 2 | 7 | 0.1 | 0 | 0 |
TRIAL_SUP02 | S03 | 9 | 0.86 | 5 | 0.7 | 0.08 | 4 | 16 | 0.04 | 1 | 1 |
TRIAL_SUP02 | S03 | 11 | 0.8 | 7 | 0.9 | 0.11 | 6 | 22 | -0.01 | 1 | 1 |
TRIAL_SUP02 | S04 | 6 | 0.91 | 3 | 0.4 | 0.05 | 3 | 10 | 0.08 | 0 | 0 |
Clinical Quad Supply Chain Dose Interruptions Exposure Gaps Efficacy Loss v0.1
Each row is a site week snapshot.
Core quad
Supply chain reliability
Dose interruptions
Exposure gaps
Efficacy loss
Target
label_efficacy_loss_next_60d
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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