ClarusC64/clinical-quad-side-effect-load-visit-burden-distance-contact-latency-dropout-event-v0.1

What this repo does

This dataset models dropout risk cascade in clinical trials. It predicts when the interaction between side effect burden, visit burden, travel distance, and delayed site contact increases the probability that a patient drops out of the trial.

Core quad

sideeffectloadindex visitburdenindex traveldistancekm sitecontactlatencydays

Prediction target

labeldropoutevent

Row structure

Each row represents a patient participation snapshot during ongoing follow-up. The model predicts whether the coupled burden and support conditions trigger a dropout event within the next scheduling window.

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.