ClarusC64/clinical-5node-inflam-buf-lag-cpl-septic-shock-v0.1

What this repo does

This dataset models a septic shock cascade by measuring when inflammatory pressure rises, physiologic buffer capacity erodes, clinical response lags, and ward-to-ICU coupling tightens, crossing a phase transition into an unrecoverable cascade state.

Core quad

inflam buf lag cpl

Prediction target

labelcascadestate

Row structure

One row represents a clinical deterioration scenario with numeric signals for inflammatory pressure (inflam), buffer capacity (buf), governance/response lag (lag), and system coupling tightness (cpl), paired with a binary cascade state label that marks the phase transition into septic shock cascade conditions.

Files

data/train.csv data/tester.csv scorer.py

Evaluation

Run predictions on tester.csv. 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.