---
language:
- en
license: mit
task_categories:
- text-classification
tags:
- clinical-trials
- cascade-modeling
- shock
- boundary-discovery
- five-node
size_categories:
- 1K<n<10K
pretty_name: Clinical Five Node Shock Cascade Boundary v0.4
---

# What this repo does

This dataset models shock cascade instability boundaries using a five-node physiological interaction system.

Clarus v0.4 datasets focus on detecting whether systems lie on the edge of cascade instability.

The objective is to determine when the shock system is so close to collapse that even small perturbations trigger systemic failure.

# Core cascade nodes

hemodynamic_stress  
vascular_buffer  
intervention_delay  
organ_coupling  
metabolic_stress

These nodes represent interacting components of shock physiology.

hemodynamic_stress captures circulatory strain and perfusion instability.

vascular_buffer represents remaining vascular reserve and compensatory capacity.

intervention_delay reflects delayed fluids, vasopressors, source control, or corrective treatment.

organ_coupling represents propagation of dysfunction across organ systems.

metabolic_stress represents systemic metabolic instability under shock conditions.

# Trajectory layer

drift_gradient

Range  
-1 to +1

Negative values indicate stabilization.

Positive values indicate drift toward cascade.

# Dynamic forecasting layer

drift_velocity  
drift_acceleration  
boundary_distance

These describe how quickly the system is approaching collapse.

# Boundary discovery layer

Two variables capture proximity to instability.

perturbation_radius  
collapse_trigger

These convert the dataset into an adversarial cascade boundary discovery benchmark.

# Boundary variable definitions

## perturbation_radius

Minimum perturbation needed to push the system into cascade.

Range 0 to 1.

Small values indicate extreme fragility.

## collapse_trigger

Binary indicator showing whether the perturbation produced cascade.

0 stable  
1 cascade

collapse_trigger is included as an observed perturbation response feature.

It is not the prediction target.

The prediction task is to identify the underlying boundary-risk state.

# Prediction target

label_shock_cascade

A positive label is triggered when either condition holds.

boundary_distance < 0.10

or

perturbation_radius < 0.08

These thresholds represent proximity to the instability manifold and minimal perturbation collapse risk.

# Row structure

scenario_id

hemodynamic_stress  
vascular_buffer  
intervention_delay  
organ_coupling  
metabolic_stress

drift_gradient  
drift_velocity  
drift_acceleration  
boundary_distance

perturbation_radius  
collapse_trigger

label_shock_cascade

# Files

data/train.csv  
labeled training examples

data/tester.csv  
unlabeled evaluation examples

scorer.py  
binary boundary detection evaluation script

README.md  
dataset documentation

# Evaluation

The scorer reports

accuracy  
precision  
recall_boundary_detection  
false_safe_rate  
f1  
confusion_matrix

Primary metric

recall_boundary_detection

Secondary diagnostic metric

false_safe_rate

# Structural Note

Clarus dataset progression

v0.1 cascade detection  
v0.2 trajectory detection  
v0.3 dynamic forecasting  
v0.4 boundary discovery

# Production Deployment

Research dataset for instability detection and cascade modeling.

Not intended for clinical decision use.

# Enterprise & Research Collaboration

For dataset expansion, custom coherence scorers, or deployment architecture:

team@clarusinvariant.com

Instability is detectable.  
Governance determines whether it propagates.