---
language:
  - en
license: mit
task_categories:
  - text-classification
tags:
  - clinical-trials
  - clarus
  - five-node-cascade
  - trajectory-aware
  - intervention-pathway
  - shock
  - boundary
size_categories:
  - 1K<n<10K
pretty_name: Clarus v0.6 Clinical Five-Node Shock Cascade Intervention Pathway Dataset
---

# What this repo does

This repository contains a Clarus v0.6 intervention pathway dataset focused on shock cascade boundary dynamics.

The dataset evaluates whether a model can determine if a proposed intervention meaningfully stabilizes a deteriorating shock system represented as a five-node cascade.

The task requires reasoning from:

- multi-node system state
- trajectory toward instability
- boundary geometry
- recovery geometry
- intervention vector
- projected trajectory consequence

The model cannot read the answer directly.

It must infer stabilization from the structure of the case.

This shifts the benchmark from simple shock cascade boundary detection to intervention reasoning across a coupled five-node system.

# Core five-node cascade

The shock cascade boundary system is represented using five normalized variables.

- shock_pressure
- physiological_buffer
- intervention_lag
- organ_coupling
- hemodynamic_instability

These variables capture the main structural drivers of shock escalation and collapse propagation.

# Clinical variable mapping

The normalized cascade variables correspond to measurable clinical signals.

| Cascade Variable | Clinical Measurements | Typical Indicators |
|------------------|----------------------|-------------------|
| shock_pressure | Lactate trend<br>Perfusion deficit burden<br>Shock index rise<br>Progressive tissue hypoperfusion | Lactate rising<br>Shock index elevated<br>Poor capillary refill |
| physiological_buffer | Albumin status<br>Cardiopulmonary reserve<br>Renal reserve<br>Frailty-adjusted resilience | Low reserve<br>Comorbidity burden<br>Poor compensatory tolerance |
| intervention_lag | Delay to fluids<br>Delay to vasopressors<br>Delay to hemorrhage or source control<br>Delay to organ-support escalation | Late fluids<br>Slow pressor escalation<br>Delayed control |
| organ_coupling | Cardiovascular-renal interaction<br>Respiratory-circulatory coupling<br>Inflammatory cross-organ spread<br>SOFA-linked propagation | Oliguria with hypotension<br>Respiratory stress plus shock |
| hemodynamic_instability | MAP decline<br>Vasopressor burden<br>Pulse pressure collapse<br>Perfusion failure burden | MAP < 65<br>Escalating pressors<br>Cold peripheries |

These measurements illustrate how normalized values in the dataset map to real shock cascade physiology.

# Prediction target

The target column is:

`label_shock_stabilization`

This label indicates whether the intervention pathway produces genuine stabilization.

# Label logic

## Default benchmark rule

A row is labeled positive only when both conditions hold:

`stabilization_success = 1`

and

`trajectory_shift < -0.10`

This rule filters out marginal corrections and ensures that positive examples represent meaningful stabilization.

## Optional relaxed rule

Positive labels may trigger when:

`stabilization_success = 1`

This relaxed rule may be used for exploratory builds but is not the default benchmark configuration.

# Row structure

Each row contains:

- five-node shock state
- trajectory geometry
- perturbation geometry
- recovery geometry
- intervention vector
- projected trajectory consequence

Train rows include:

- `stabilization_success`
- `label_shock_stabilization`

Tester rows exclude these fields.

# Why tester rows exclude stabilization_success

The tester file withholds:

- `stabilization_success`
- `label_shock_stabilization`

This prevents answer leakage.

The model must infer stabilization using:

- the starting five-node shock state
- drift toward the failure boundary
- recovery basin geometry
- the intervention vector
- the predicted trajectory consequence

This structure forces real intervention reasoning.

# Minimal intervention path

`minimal_intervention_path` encodes the shortest viable stabilization pathway.

Example interpretation:

- `0` = no viable rescue path
- `1` = direct stabilization pathway
- `2` = multi-step stabilization sequence
- `3` = complex high-risk rescue pathway

The field remains visible because the benchmark evaluates whether the model can combine intervention structure with trajectory consequence to determine stabilization.

# Files

- `data/train.csv` — labeled training dataset
- `data/tester.csv` — unlabeled benchmark dataset with withheld stabilization signal
- `scorer.py` — evaluation metrics and confusion matrix computation
- `cli.py` — command-line evaluation wrapper used for benchmark scoring
- `README.md` — dataset card and schema documentation

# Evaluation

The scorer reports:

- `accuracy`
- `precision`
- `recall_successful_stabilization`
- `failed_rescue_rate`
- `f1`
- `confusion_matrix`

Primary metric:

`recall_successful_stabilization`

Secondary metric:

`failed_rescue_rate`

Interpretation:

`recall_successful_stabilization` measures how reliably the model detects interventions that genuinely stabilize the shock five-node cascade.

`failed_rescue_rate` measures how often the model fails to recognize a viable stabilization pathway.

These metrics prioritize intervention reasoning rather than generic classification accuracy.

# Schema

## train.csv columns

- `scenario_id`
- `shock_pressure`
- `physiological_buffer`
- `intervention_lag`
- `organ_coupling`
- `hemodynamic_instability`
- `drift_gradient`
- `drift_velocity`
- `drift_acceleration`
- `boundary_distance`
- `perturbation_radius`
- `collapse_trigger`
- `recovery_distance`
- `recovery_gradient`
- `return_feasibility`
- `delta_shock_pressure`
- `delta_physiological_buffer`
- `delta_intervention_lag`
- `delta_organ_coupling`
- `delta_hemodynamic_instability`
- `trajectory_shift`
- `minimal_intervention_path`
- `stabilization_success`
- `label_shock_stabilization`

## tester.csv columns

- `scenario_id`
- `shock_pressure`
- `physiological_buffer`
- `intervention_lag`
- `organ_coupling`
- `hemodynamic_instability`
- `drift_gradient`
- `drift_velocity`
- `drift_acceleration`
- `boundary_distance`
- `perturbation_radius`
- `collapse_trigger`
- `recovery_distance`
- `recovery_gradient`
- `return_feasibility`
- `delta_shock_pressure`
- `delta_physiological_buffer`
- `delta_intervention_lag`
- `delta_organ_coupling`
- `delta_hemodynamic_instability`
- `trajectory_shift`
- `minimal_intervention_path`

# Structural note

The Clarus dataset series evolves through progressively richer representations of cascade dynamics.

Version progression:

- v0.1 — cascade state detection
- v0.2 — trajectory-aware detection
- v0.3 — dynamic cascade forecasting
- v0.4 — boundary discovery
- v0.5 — recovery geometry
- v0.6 — intervention pathway reasoning

The five-node cascade format extends this stack from quad structure to higher-order coupled instability modeling.

Version 0.6 evaluates whether a proposed intervention meaningfully alters the trajectory of a five-node shock system approaching collapse.

This marks the transition from cascade monitoring to coupled control reasoning.

# Production deployment

This dataset structure can support clinical decision environments where shock cascade boundary breach must be detected and corrected before irreversible collapse occurs.

Example settings include:

- emergency shock triage
- ICU shock escalation review
- hemorrhagic or distributive shock monitoring
- vasopressor and fluids step-up surveillance
- multi-organ collapse prevention

# Enterprise and research collaboration

This dataset class supports benchmarking for:

- intervention-aware clinical AI
- five-node shock cascade modeling
- recovery feasibility prediction
- false-stability detection
- boundary-sensitive decision support systems

# Contact

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

team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.

# License

MIT