scenario_id int64 | hemodynamic_pressure float64 | physiological_buffer float64 | intervention_delay float64 | organ_coupling float64 | perfusion_instability float64 | drift_gradient float64 | drift_velocity float64 | drift_acceleration float64 | boundary_distance float64 | perturbation_radius float64 | collapse_trigger int64 | recovery_distance float64 | recovery_gradient float64 | return_feasibility int64 | label_shock_cascade int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0.86 | 0.21 | 0.67 | 0.75 | 0.73 | 0.74 | 0.64 | 0.38 | 0.04 | 0.02 | 1 | 0.81 | 0.52 | 0 | 1 |
2 | 0.77 | 0.3 | 0.58 | 0.69 | 0.64 | 0.55 | 0.48 | 0.22 | 0.08 | 0.05 | 0 | 0.64 | 0.18 | 1 | 0 |
3 | 0.69 | 0.36 | 0.52 | 0.61 | 0.58 | 0.4 | 0.37 | 0.15 | 0.13 | 0.07 | 0 | 0.45 | -0.12 | 1 | 0 |
4 | 0.82 | 0.25 | 0.63 | 0.72 | 0.69 | 0.66 | 0.57 | 0.3 | 0.05 | 0.03 | 1 | 0.73 | 0.39 | 0 | 1 |
5 | 0.6 | 0.44 | 0.45 | 0.54 | 0.5 | 0.16 | 0.26 | 0.03 | 0.2 | 0.1 | 0 | 0.26 | -0.36 | 1 | 0 |
6 | 0.74 | 0.33 | 0.55 | 0.65 | 0.62 | 0.48 | 0.42 | 0.19 | 0.1 | 0.06 | 0 | 0.52 | 0.06 | 1 | 0 |
7 | 0.91 | 0.17 | 0.75 | 0.81 | 0.8 | 0.81 | 0.7 | 0.43 | 0.02 | 0.01 | 1 | 0.87 | 0.58 | 0 | 1 |
8 | 0.65 | 0.4 | 0.49 | 0.57 | 0.54 | 0.23 | 0.3 | 0.07 | 0.17 | 0.09 | 0 | 0.33 | -0.25 | 1 | 0 |
9 | 0.88 | 0.2 | 0.69 | 0.77 | 0.75 | 0.76 | 0.66 | 0.39 | 0.03 | 0.02 | 1 | 0.82 | 0.49 | 0 | 1 |
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:
Instability is detectable.
Governance determines whether it propagates.
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