---
language: en
license: mit
task_categories:
- text-classification
tags:
- clinical-trials
- five-node-cascade
- cascade-recovery
- shock
size_categories:
- 1K<n<10K
pretty_name: Clinical Five Node Shock Cascade Boundary v0.5
---

# What this repo does

This repository provides a Clarus v0.5 cascade recovery geometry dataset modeling shock cascade transition with a five-node clinical structure.

Earlier Clarus datasets focused on state detection and boundary discovery.

Version v0.5 adds a recovery geometry layer that asks a stricter question:

Can the system still return to stability?

The task is binary classification over shock-linked deterioration states using:

• a five-node clinical cascade  
• trajectory dynamics  
• boundary discovery signals  
• recovery geometry variables

Models must determine whether the shock cascade remains recoverable or has crossed into irreversible deterioration.

---

# Core five-node cascade

The core five-node structure for this dataset is:

• hemodynamic_pressure  
• physiological_buffer  
• intervention_delay  
• organ_coupling  
• perfusion_instability

Operational interpretation:

hemodynamic_pressure  
Represents circulatory burden such as hypotensive stress, vasoplegia, preload loss, or escalating shock pressure.

physiological_buffer  
Represents physiological reserve available to absorb hemodynamic insult.

intervention_delay  
Captures delay before fluids, vasopressors, transfusion, source control, or other stabilizing interventions.

organ_coupling  
Represents propagation of dysfunction across interacting organ systems.

perfusion_instability  
Represents tissue hypoperfusion, microcirculatory failure, and worsening shock propagation.

---

## Terminology note

Earlier Clarus variants may use related circulatory naming such as perfusion_pressure.

In v0.5 this node is expressed as perfusion_instability.

The rename shifts emphasis from raw pressure level toward broader instability of perfusion dynamics while preserving the same normalized 0 to 1 scale.

---

# Trajectory layer

The dataset includes a trajectory signal:

drift_gradient

Range:

−1 to +1

Interpretation:

negative values indicate motion toward recovery  
positive values indicate motion toward deterioration

This lets the model infer directional movement rather than assess a static snapshot alone.

---

# Dynamic forecasting layer

Three dynamic variables describe system motion:

• drift_velocity  
• drift_acceleration  
• boundary_distance

These variables allow models to reason about how quickly the system is moving and how near it lies to the cascade boundary.

---

# Boundary discovery layer

The dataset retains the boundary discovery layer introduced in v0.4.

Variables:

• perturbation_radius  
• collapse_trigger

Interpretation:

perturbation_radius  
Measures how much disturbance the system can absorb before crossing into collapse.

collapse_trigger  
Binary indicator that the instability boundary has been crossed.

---

# Recovery geometry layer

v0.5 introduces a recovery geometry layer that determines whether recovery remains possible.

Variables:

• recovery_distance  
• recovery_gradient  
• return_feasibility

These variables transform the task from collapse detection into recovery reasoning.

Models must determine not only whether a system is unstable, but whether a path back to stability still exists.

---

# Recovery variable definitions

## recovery_distance

Distance from the current system state to the nearest stable basin.

Definition:

`recovery_distance = min ||x - x_stable||`

Range:

0 to 1

Interpretation:

small values indicate proximity to a recoverable region  
large values indicate deep cascade penetration

---

## recovery_gradient

Direction of motion relative to the nearest recovery basin.

Range:

−1 to +1

Interpretation:

negative values indicate motion toward recovery  
positive values indicate motion deeper into collapse

---

## return_feasibility

Binary indicator representing whether recovery remains possible.

Values:

0  
system has crossed an irreversible cascade boundary

1  
system still lies within a recoverable region

---

# Prediction target

Target column:

`label_shock_cascade`

A positive label indicates irreversible shock cascade transition.

## Collapse threshold

The cascade boundary threshold used for labeling is:

`collapse_threshold = 0.05`

## Label logic

Positive labels trigger when either condition holds:

`boundary_distance < 0.05`

or

`return_feasibility = 0`

This encodes irreversible cascade detection.

---

# Binary simplification note

The underlying system dynamics are continuous and multi-dimensional.

For benchmark clarity, the dataset compresses this structure into a binary classification task:

recoverable state  
versus  
irreversible deterioration

The recovery geometry variables preserve the deeper system structure.

---

# Row structure

Each dataset row contains:

scenario_id

hemodynamic_pressure  
physiological_buffer  
intervention_delay  
organ_coupling  
perfusion_instability

drift_gradient  
drift_velocity  
drift_acceleration  
boundary_distance

perturbation_radius  
collapse_trigger

recovery_distance  
recovery_gradient  
return_feasibility

label_shock_cascade

---

# Variable ranges

State variables

0 to 1

drift_gradient

−1 to +1

drift_velocity

0 to 1

drift_acceleration

−1 to +1

boundary_distance

0 to 1

perturbation_radius

0 to 1

collapse_trigger

0 or 1

recovery_distance

0 to 1

recovery_gradient

−1 to +1

return_feasibility

0 or 1

---

# Files

data/train.csv  
Labeled training examples.

data/tester.csv  
Unlabeled test scenarios.

scorer.py  
Evaluation script for binary classification.

cli.py  
Optional command-line wrapper for running the scorer.

README.md  
Dataset documentation.

---

# Evaluation

The scorer reports the following metrics:

accuracy  
precision  
recall_irreversible_detection  
false_recovery_rate  
f1  
confusion_matrix

Primary metric

recall_irreversible_detection

This metric prioritizes detection of irreversible deterioration.

Secondary diagnostic metric

false_recovery_rate

This measures how often irreversible states are incorrectly treated as recoverable.

---

# Version progression

Clarus datasets evolve through successive capability layers.

v0.1  
Cascade state detection datasets

v0.2  
Cascade + trajectory datasets

v0.3  
Cascade + trajectory + dynamic forecasting datasets

v0.4  
Cascade + trajectory + dynamics + boundary discovery datasets

v0.5  
Cascade + trajectory + dynamics + boundary discovery + recovery geometry datasets

Earlier versions remain unchanged to preserve benchmark continuity.

---

# License

MIT

---

# Structural Note

Clarus v0.5 marks the transition from instability mapping to recovery geometry.

Earlier datasets asked whether systems were approaching collapse.

v0.5 asks a more operational question:

Is recovery still structurally possible?

This makes the dataset class closer to real-world decision support systems.

---

# Production Deployment

Recovery geometry datasets are suitable for applications where distinguishing recoverable shock states from irreversible cascade is critical.

Possible domains include:

shock escalation monitoring  
critical care surveillance  
perfusion rescue pathway modeling  
intervention timing simulation

---

# Enterprise & Research Collaboration

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

team@clarusinvariant.com

Instability is detectable.  
Governance determines whether it propagates.