Datasets:

ClarusC64
/

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

scenario_id string	metabolic_stress float64	physiologic_buffer float64	inflammatory_lag float64	hemodynamic_coupling float64	organ_coupling float64	drift_gradient float64	drift_velocity float64	drift_acceleration float64	boundary_distance float64	secondary_boundary_distance float64	boundary_competition_ratio float64	boundary_uncertainty float64	trajectory_uncertainty float64	regime_confidence float64	regime_transition_score float64	transition_direction string	regime_separation_margin float64	transition_uncertainty float64	transition_velocity float64	intervention_leverage_score float64	intervention_alignment_score float64	rescue_window_width float64	pathway_divergence_margin float64	intervention_competition_ratio float64	primary_intervention_path string	secondary_intervention_path string	intervention_uncertainty float64	pathway_switch_velocity float64	perturbation_radius float64	collapse_trigger string	recovery_distance float64	recovery_gradient float64	return_feasibility float64	delta_metabolic_stress float64	delta_physiologic_buffer float64	delta_inflammatory_lag float64	delta_hemodynamic_coupling float64	delta_organ_coupling float64	trajectory_shift float64	minimal_intervention_path string	stabilization_success int64	label_mof_cascade_boundary_stabilization int64
MOFC_001	0.89	0.27	0.74	0.81	0.83	0.74	0.66	0.23	0.15	0.24	0.63	0.18	0.24	0.86	0.81	toward_multiorgan_failure	0.3	0.2	0.21	0.91	0.94	0.2	0.38	0.4	source_control_then_perfusion_support_then_dialysis	vasopressors_then_perfusion_support_then_dialysis	0.12	0.18	0.82	septic_hypoperfusion_with_aki	0.18	-0.55	0.9	0.23	-0.09	0.18	0.17	0.16	-0.46	source_control_then_perfusion_support_then_dialysis	1	1
MOFC_002	0.82	0.31	0.66	0.73	0.75	0.64	0.57	0.18	0.21	0.29	0.72	0.21	0.22	0.78	0.68	toward_renal_metabolic_failure	0.19	0.23	0.15	0.66	0.58	0.24	0.1	0.89	fluids_then_perfusion_support_then_dialysis	dialysis_then_perfusion_support_then_source_control	0.27	0.15	0.69	lactate_rise_with_aki_drift	0.34	-0.18	0.49	0.14	-0.05	0.11	0.1	0.09	-0.08	fluids_then_perfusion_support_then_dialysis	0	0
MOFC_003	0.76	0.39	0.61	0.64	0.65	0.42	0.35	0.09	0.35	0.47	0.74	0.15	0.17	0.73	0.57	toward_hepatorenal_transition	0.24	0.16	0.08	0.8	0.85	0.39	0.24	0.58	perfusion_support_then_source_control_then_dialysis	dialysis_then_perfusion_support_then_source_control	0.14	0.1	0.56	hepatic_strain_with_aki	0.28	-0.32	0.79	0.1	-0.04	0.08	0.07	0.06	-0.27	perfusion_support_then_source_control_then_dialysis	1	1
MOFC_004	0.92	0.23	0.79	0.86	0.87	0.78	0.7	0.25	0.12	0.21	0.67	0.19	0.28	0.89	0.84	toward_refractory_mof	0.32	0.18	0.22	0.94	0.97	0.18	0.41	0.37	source_control_then_perfusion_support_then_dialysis	dialysis_then_perfusion_support_then_source_control	0.11	0.21	0.85	progressive_septic_mof	0.15	-0.58	0.92	0.24	-0.1	0.19	0.18	0.18	-0.49	source_control_then_perfusion_support_then_dialysis	1	1
MOFC_005	0.68	0.48	0.54	0.57	0.58	0.27	0.21	0.05	0.45	0.53	0.85	0.12	0.14	0.66	0.46	toward_low_grade_metabolic_instability	0.23	0.18	0.04	0.44	0.57	0.42	0.06	0.95	glucose_control_then_observation_then_fluids	fluids_then_observation_then_glucose_control	0.24	0.08	0.46	mild_metabolic_drift	0.4	-0.1	0.58	0.06	-0.03	0.04	0.03	0.03	-0.05	glucose_control_then_observation_then_fluids	0	0
MOFC_006	0.87	0.29	0.69	0.75	0.76	0.69	0.61	0.21	0.17	0.25	0.68	0.2	0.23	0.82	0.73	toward_lactic_acidosis_mof	0.24	0.19	0.17	0.85	0.88	0.27	0.12	0.86	perfusion_support_then_source_control_then_dialysis	dialysis_then_perfusion_support_then_source_control	0.26	0.17	0.76	persistent_lactate_with_aki	0.28	-0.16	0.6	0.16	-0.06	0.11	0.1	0.1	-0.09	perfusion_support_then_source_control_then_dialysis	0	0
MOFC_007	0.79	0.35	0.67	0.66	0.67	0.51	0.43	0.13	0.29	0.39	0.74	0.17	0.19	0.74	0.61	toward_respiratory_metabolic_mof	0.21	0.17	0.1	0.77	0.82	0.34	0.19	0.64	nivincrease_then_perfusion_support_then_source_control	intubation_then_perfusion_support_then_dialysis	0.16	0.09	0.61	co2_retention_with_hemodynamic_drift	0.27	-0.27	0.78	0.12	-0.04	0.09	0.06	0.06	-0.22	nivincrease_then_perfusion_support_then_source_control	1	1
MOFC_008	0.84	0.28	0.75	0.8	0.81	0.68	0.6	0.21	0.16	0.24	0.67	0.18	0.24	0.83	0.76	toward_septic_metabolic_transition	0.2	0.2	0.18	0.81	0.66	0.23	0.07	0.88	source_control_then_perfusion_support_then_vasopressors	fluids_then_vasopressors_then_source_control	0.29	0.19	0.77	abdominal_source_with_metabolic_drift	0.35	-0.14	0.59	0.13	-0.05	0.12	0.11	0.11	-0.07	fluids_then_vasopressors_then_source_control	0	0
MOFC_009	0.74	0.4	0.59	0.62	0.63	0.38	0.32	0.08	0.37	0.49	0.76	0.14	0.16	0.72	0.54	toward_mixed_metabolic_failure	0.23	0.16	0.07	0.82	0.87	0.39	0.22	0.59	dialysis_then_perfusion_support_then_source_control	perfusion_support_then_source_control_then_dialysis	0.15	0.12	0.54	aki_with_acidosis_and_hypoperfusion	0.3	-0.29	0.8	0.1	-0.04	0.08	0.07	0.07	-0.24	dialysis_then_perfusion_support_then_source_control	1	1

Clinical Five Node MOF Cascade Boundary (v0.9)

What this repo does

This dataset implements a Clarus v0.9 intervention-competition benchmark across a five-node multiorgan failure cascade.

Earlier dataset versions focused on detecting:

deterioration
regime transitions
boundary proximity
recovery feasibility

v0.9 extends the ladder.

The benchmark now evaluates whether a model can identify the correct rescue path when multiple interventions compete under narrowing rescue windows.

This reflects real system decision geometry in cascading multiorgan failure states.

In real systems:

several plausible interventions may exist
one intervention may have slightly higher structural leverage
intervention rankings can change rapidly
selecting the wrong path can waste the rescue window

v0.9 explicitly models intervention competition.

Core five-node cascade

The instability geometry is defined by five interacting variables.

Cascade Variable	Measurement Sources	Typical Indicators
metabolic_stress	Lactate, base deficit, glucose instability, acid-base load	Lactate rising, base deficit worsening, severe dysglycaemia
physiologic_buffer	Albumin, haemoglobin, reserve capacity, perfusion reserve	Albumin low, Hb low, reduced reserve, poor clearance
inflammatory_lag	CRP trend, fever curve, WBC trajectory, cytokine burden	CRP rising, fever > 38.5, WBC > 12
hemodynamic_coupling	MAP drift, vasopressor need, capillary refill, shock burden	Hypotension, rising pressor need, poor perfusion
organ_coupling	Creatinine drift, bilirubin drift, ABG deterioration, urine output	AKI progression, bilirubin rising, oliguria, gas exchange worsening

These five variables define the local stability geometry of the multiorgan failure cascade.

Example intervention competition scenario

A typical v0.9 row captures a situation where two or more rescue paths compete.

Example:

Primary intervention path

source_control_then_perfusion_support_then_dialysis

Secondary intervention path

vasopressors_then_perfusion_support_then_dialysis

Example signals

intervention_alignment_score ≈ 0.82
intervention_leverage_score ≈ 0.79
pathway_divergence_margin ≈ 0.07
intervention_competition_ratio ≈ 0.91
rescue_window_width ≈ 0.24

Interpretation

multiple plausible rescue paths exist
the primary path has slightly stronger structural leverage
the rescue window is narrowing
selecting the wrong path risks losing recoverability

This geometry is exactly what the v0.9 benchmark tests.

Prediction target

Label column

label_mof_cascade_boundary_stabilization

Default rule

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

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