ClarusC64/clinical-five-node-mof-cascade-boundary-v0.9

label = 1 if stabilization_success = 1 AND trajectory_shift < -0.10
Relaxed variant

label = 1 if stabilization_success = 1
Preferred v0.9 rule

label = 1 if stabilization_success = 1 \
AND trajectory_shift < -0.10 \
AND intervention_alignment_score >= 0.60
The v0.9 rule rewards correctly targeted rescue, not merely recovery.

Row structure
Each dataset row describes system geometry.

State variables
metabolic_stress

physiologic_buffer

inflammatory_lag

hemodynamic_coupling

organ_coupling

Trajectory signals
drift_gradient

drift_velocity

drift_acceleration

Boundary signals
boundary_distance

secondary_boundary_distance

boundary_competition_ratio

Uncertainty signals
boundary_uncertainty

trajectory_uncertainty

intervention_uncertainty

Regime transition signals
regime_transition_score

transition_direction

regime_separation_margin

transition_uncertainty

transition_velocity

Intervention competition signals
intervention_leverage_score

intervention_alignment_score

rescue_window_width

pathway_divergence_margin

intervention_competition_ratio

primary_intervention_path

secondary_intervention_path

intervention_uncertainty

pathway_switch_velocity

Recovery signals
recovery_distance

recovery_gradient

return_feasibility

Perturbation signals
perturbation_radius

collapse_trigger

Delta signals
delta_metabolic_stress

delta_physiologic_buffer

delta_inflammatory_lag

delta_hemodynamic_coupling

delta_organ_coupling

Outcome fields
trajectory_shift

minimal_intervention_path

stabilization_success

Label
label_mof_cascade_boundary_stabilization

Files
data/train.csv
Training rows including:

stabilization_success

label column

data/tester.csv
Evaluation rows excluding:

stabilization_success

label column

scorer.py
The scorer evaluates both classification accuracy and intervention reasoning.

Binary metrics

accuracy

precision

recall

f1

confusion_matrix

Primary v0.9 metrics

recall_correct_intervention_selection

false_effective_intervention_rate

Intervention competition diagnostics

primary_intervention_path_accuracy

secondary_intervention_path_accuracy

intervention_alignment_accuracy

high_uncertainty_intervention_miss_rate

rescue_window_miss_rate

intervention_competition_error

pathway_switch_misread_rate

benchmark_spec.json
Machine-readable dataset specification including:

schema

thresholds

signal groups

evaluation interface

Dataset construction
Dataset rows are generated in stages.

Stage 1 — baseline cascade state
Five-node variables are sampled to represent plausible multiorgan failure states.

Stage 2 — trajectory dynamics
Drift signals describe movement toward or away from instability.

Stage 3 — boundary and regime detection
Boundary and transition signals describe proximity to instability basins across the cascade.

Stage 4 — intervention competition modelling
Multiple intervention paths are generated.

For each candidate path:

intervention_alignment_score measures regime compatibility

intervention_leverage_score estimates trajectory impact

pathway_divergence_margin measures separation between paths

intervention_competition_ratio captures relative leverage

rescue_window_width represents remaining recoverability

The best path becomes:

primary_intervention_path

The second-best becomes:

secondary_intervention_path

Dataset limitations
This dataset models structural intervention geometry, not detailed intervention pharmacology.

Limitations include:

intervention_alignment_score represents structural compatibility rather than exact treatment efficacy

rescue_window_width represents geometric recoverability rather than clock time

intervention paths represent simplified bundles of actions

cascade coupling is structural and does not capture every bedside timing dependency

These abstractions allow intervention competition geometry to generalize across domains.

Intended use
This dataset is intended for:

research on intervention selection under instability

evaluating regime-aware reasoning systems

testing models under narrowing rescue windows

benchmarking control-style decision models

studying cascade-aware action selection in multiorgan failure

This dataset is not intended for:

clinical decision support in patient care

treatment recommendation systems

medical device deployment

regulatory evaluation

It is a research benchmark.

Clarus ladder context
Clarus datasets follow an instability-geometry ladder.

v0.1 cascade detection
v0.2 trajectory awareness
v0.3 cascade forecasting
v0.4 boundary discovery
v0.5 recovery geometry
v0.6 intervention reasoning
v0.7 uncertainty-aware intervention
v0.8 regime transition geometry
v0.9 intervention competition geometry

v0.9 marks the transition toward control-layer reasoning benchmarks.

Structural note
Clarus benchmarks represent instability geometry directly.

This dataset frames collapse as competition between:

failure pressure

intervention leverage

rescue window narrowing

In a five-node cascade this produces a richer control problem than a simple classifier.

Production deployment
This benchmark structure helps models distinguish between:

a plausible intervention

the best intervention

a nearly tied but weaker intervention

an intervention that becomes obsolete as the rescue window closes

This geometry appears in many domains including:

multiorgan failure monitoring

ICU escalation logic

infrastructure failure mitigation

supply chain disruption recovery

AI system stabilization protocols

Enterprise and research collaboration
The v0.9 intervention competition structure generalizes across domains where multiple rescue paths compete under instability pressure.

Possible extensions include:

multiorgan cascade control

septic shock escalation monitoring

respiratory deterioration modelling

infrastructure recovery planning

financial crisis intervention sequencing

AI system resilience benchmarking