Datasets:

ClarusC64
/

clinical-five-node-mof-cascade-boundary-v0.8

scenario_id string	metabolic_stress float64	physiologic_buffer float64	response_lag float64	organ_coupling float64	perfusion_stability 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	perturbation_radius float64	collapse_trigger string	recovery_distance float64	recovery_gradient float64	return_feasibility float64	delta_metabolic_stress float64	delta_physiologic_buffer float64	delta_response_lag float64	delta_organ_coupling float64	delta_perfusion_stability float64	trajectory_shift float64	minimal_intervention_path string	stabilization_success int64	label_mof_cascade_boundary int64
MOFCAS001	0.83	0.3	0.73	0.78	0.34	0.48	0.41	0.22	0.16	0.19	1.19	0.13	0.18	0.63	0.64	metabolic_overload_to_renal_hepatic_failure	0.1	0.18	0.25	0.29	lactate_escalation	0.25	-0.15	0.57	-0.13	0.1	-0.09	-0.08	0.11	-0.16	optimize perfusion; insulin and electrolyte correction; renal-hepatic review	1	1
MOFCAS002	0.9	0.21	0.82	0.85	0.2	0.67	0.58	0.35	0.09	0.1	1.11	0.2	0.24	0.47	0.8	metabolic_overload_to_multiorgan_failure	0.07	0.24	0.36	0.37	refractory_acidosis	0.37	-0.04	0.26	-0.05	0.02	-0.03	-0.02	0.03	-0.06	vasopressor support; dialysis review; invasive monitoring; ICU escalation	0	0
MOFCAS003	0.76	0.38	0.64	0.69	0.41	0.3	0.25	0.12	0.24	0.37	1.54	0.1	0.13	0.72	0.36	metabolic_overload_to_renal_hepatic_failure	0.17	0.13	0.15	0.2	progressive_azotemia	0.21	-0.13	0.68	-0.1	0.09	-0.08	-0.07	0.08	-0.14	fluids; glucose control; repeat renal and liver function	1	1
MOFCAS004	0.94	0.17	0.89	0.91	0.17	0.78	0.67	0.44	0.07	0.08	1.14	0.23	0.28	0.39	0.87	metabolic_failure_to_refractory_mof	0.05	0.29	0.43	0.41	refractory_hypoperfusion	0.42	-0.02	0.18	-0.03	0.01	-0.01	0	0.01	-0.03	maximal organ support; continuous RRT review; invasive monitoring; urgent senior review	0	0
MOFCAS005	0.7	0.45	0.55	0.6	0.5	0.21	0.18	0.07	0.33	0.49	1.48	0.08	0.1	0.81	0.24	stable_metabolic_stress_to_slow_decline	0.22	0.09	0.1	0.16	catabolic_drift	0.17	-0.11	0.76	-0.08	0.07	-0.06	-0.05	0.06	-0.12	repeat labs; nutrition review; fluid balance reassessment	1	1
MOFCAS006	0.87	0.25	0.79	0.82	0.26	0.59	0.51	0.3	0.13	0.14	1.08	0.17	0.21	0.54	0.77	metabolic_overload_to_multiorgan_failure	0.08	0.21	0.31	0.33	hyperkalemic_instability	0.32	-0.05	0.33	-0.05	0.03	-0.03	-0.01	0.03	-0.07	electrolyte correction; dialysis consideration; perfusion review	0	0
MOFCAS007	0.74	0.41	0.6	0.65	0.39	0.26	0.21	0.09	0.28	0.33	1.18	0.11	0.12	0.77	0.47	metabolic_overload_to_renal_hepatic_failure	0.14	0.13	0.17	0.21	uremic_progression	0.19	-0.14	0.7	-0.09	0.08	-0.07	-0.06	0.07	-0.15	renal support watch; medication adjustment; organ support review	1	1
MOFCAS008	0.95	0.15	0.91	0.92	0.14	0.84	0.72	0.5	0.06	0.07	1.17	0.25	0.3	0.35	0.89	metabolic_failure_to_refractory_mof	0.05	0.3	0.47	0.43	refractory_acidemia	0.44	-0.01	0.15	-0.01	0	0	0.01	0.01	0	full organ support bundle; ICU transfer; urgent dialysis pathway review	0	0
MOFCAS009	0.68	0.47	0.53	0.57	0.53	0.18	0.15	0.05	0.36	0.52	1.44	0.07	0.09	0.83	0.19	stable_metabolic_stress_to_slow_decline	0.24	0.08	0.08	0.14	low_grade_catabolism	0.14	-0.12	0.79	-0.07	0.06	-0.05	-0.05	0.05	-0.13	observe; repeat bloods; nurse escalation protocol	1	1

What this repo does

This repository provides a Clarus v0.8 clinical five-node dataset for detecting and reasoning about multi-organ failure cascade boundary transitions.

The dataset models situations where a patient state is no longer contained within a single metabolic-organ failure basin but is shifting between competing regimes such as:

metabolic overload with early organ strain
renal-hepatic failure transition
perfusion-linked organ cascade
refractory multi-organ failure

This is the conceptual upgrade introduced in Clarus v0.8.

Earlier ladder versions detect instability, forecast deterioration, estimate collapse boundaries, model recovery geometry, and reason about intervention.

v0.8 introduces regime transition geometry.

The system now measures not only distance to the nearest failure boundary but also distance to the nearest competing regime boundary.

This allows models to detect:

regime switching
competing failure modes
unstable regime identity
transition-aware intervention reasoning

Core five-node cascade

The five core variables in this dataset are:

metabolic_stress
physiologic_buffer
response_lag
organ_coupling
perfusion_stability

Operational definitions:

metabolic_stress

Total metabolic burden imposed by acidosis, catabolism, electrolyte instability, and impaired substrate handling.

physiologic_buffer

Remaining reserve available to absorb metabolic insult without organ-level cascade.

response_lag

Delay in correcting metabolic derangement or restoring systemic stability.

organ_coupling

Degree to which metabolic instability is propagating into coordinated failure across organs.

perfusion_stability

Current ability to maintain pressure-flow coherence across tissues despite worsening metabolic and organ stress.

Clinical variable mapping

Variable	Clinical Measurements	Typical Indicators
metabolic_stress	Lactate, bicarbonate, glucose, anion gap, potassium	Rising lactate, worsening acidosis, hyperkalemia
physiologic_buffer	Albumin, frailty, hepatic reserve, renal reserve	Low albumin, poor reserve, impaired compensation
response_lag	Delay to insulin, fluids, dialysis, perfusion correction	Persistent acidosis, late RRT, slow correction
organ_coupling	Creatinine, bilirubin, SOFA, urine output	Rising creatinine, bilirubin increase, falling urine output
perfusion_stability	MAP, lactate clearance, capillary refill, urine output	Poor perfusion, slow lactate clearance, oliguria

These mappings are illustrative rather than prescriptive.

The dataset encodes structural system state, not a single clinical protocol.

Why the second boundary matters

Most instability detectors estimate only one quantity:

distance to the nearest collapse boundary.

This is insufficient for real MOF deterioration.

A patient may appear to be worsening within one metabolic-organ state while the deeper reality is that they are crossing into a different failure basin.

v0.8 introduces:

secondary_boundary_distance

The model now measures:

distance to the current regime boundary
distance to the competing regime boundary

This enables detection of regime competition.

When the two distances converge, regime identity becomes unstable.

The derived signal

boundary_competition_ratio

secondary_boundary_distance / boundary_distance

acts as a marker of this instability.

Values approaching 1 indicate a system positioned between regimes.

Structural Note

This dataset is part of the Clarus ladder.

The ladder reconstructs instability geometry step by step:

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

Each rung increases resolution of the system state space.

v0.8 is the first rung where competition between instability basins is explicitly modeled.

That upgrade transforms Clarus from a collapse detector into a phase-transition instrument for complex systems.

Example regime competition row

A typical v0.8 MOF cascade transition scenario looks like this.

Field	Example
metabolic_stress	0.90
physiologic_buffer	0.21
response_lag	0.82
organ_coupling	0.85
perfusion_stability	0.20
boundary_distance	0.09
secondary_boundary_distance	0.10
boundary_competition_ratio	1.11
regime_transition_score	0.80
transition_direction	metabolic_overload_to_multiorgan_failure
regime_separation_margin	0.07
transition_uncertainty	0.24
transition_velocity	0.36
collapse_trigger	refractory_acidosis

Interpretation:

The patient is close to the current regime boundary while a competing regime boundary is also nearby.

The system is no longer a simple metabolic correction problem.

It is crossing into a more dangerous cascade where perfusion failure and organ coupling become dominant.

Prediction target

The prediction target is:

label_mof_cascade_boundary

Default label rule:

label = 1 if stabilization_success = 1 and trajectory_shift < -0.10

Relaxed variant:

label = 1 if stabilization_success = 1

This preserves consistency with v0.6 and v0.7 while extending the geometry into v0.8 regime transition reasoning.

Row structure

Each dataset row contains layered signals.

Core state

metabolic_stress
physiologic_buffer
response_lag
organ_coupling
perfusion_stability

Trajectory signals

drift_gradient
drift_velocity
drift_acceleration

Boundary geometry

boundary_distance
secondary_boundary_distance
boundary_competition_ratio

Uncertainty layer

boundary_uncertainty
trajectory_uncertainty
regime_confidence

Regime transition layer

regime_transition_score
transition_direction
regime_separation_margin
transition_uncertainty
transition_velocity

Perturbation and collapse markers

perturbation_radius
collapse_trigger

Recovery geometry

recovery_distance
recovery_gradient
return_feasibility

Intervention vector

delta_metabolic_stress
delta_physiologic_buffer
delta_response_lag
delta_organ_coupling
delta_perfusion_stability

Outcome fields

trajectory_shift
minimal_intervention_path
stabilization_success
label_mof_cascade_boundary in train only

tester.csv excludes:

stabilization_success
label_mof_cascade_boundary

Files

data/train.csv

Training rows including outcome labels.

data/tester.csv

Evaluation rows with labels removed.

scorer.py

Reference v0.8 evaluation script.

Returns:

Classification metrics

accuracy
precision
recall
f1
confusion matrix

Regime transition diagnostics

recall_regime_transition_detection
false_stable_regime_rate
transition_direction_accuracy
high_uncertainty_transition_miss_rate
transition_detection_accuracy
false_transition_rate
missed_transition_rate
boundary_competition_error
misidentified_primary_regime_rate

Additional information

support counts
threshold transparency
dynamic label column discovery

benchmark_spec.json

Machine-readable benchmark contract describing task type, target label, withheld fields, metric definitions, thresholds, and prediction-file requirements.

Evaluation

Primary metrics

recall_regime_transition_detection

false_stable_regime_rate

Transition diagnostics

transition_direction_accuracy
high_uncertainty_transition_miss_rate
missed_transition_rate
boundary_competition_error
misidentified_primary_regime_rate

Additional reference diagnostics

transition_detection_accuracy
false_transition_rate

The goal of evaluation is not only to test whether a model predicts the correct label.

It tests whether the model correctly detects regime transitions and their geometry.

Note on transition_velocity

transition_velocity represents the rate at which the system moves toward the competing basin.

The variable is included in the dataset because it can help models reason about escalation speed.

The reference scorer does not yet evaluate this field directly.

Future versions may introduce velocity-aware diagnostics.

Production deployment

This dataset class functions best as a transition-aware monitoring layer rather than a standalone classifier.

Typical deployments include:

renal-metabolic deterioration monitoring
ICU escalation support for organ failure risk
digital twin modeling of metabolic-to-organ collapse
dialysis and perfusion timing analysis
AI model auditing for competing failure-mode recognition

The value lies in representing where the patient sits between competing basins.

Enterprise and research collaboration

This dataset class is suitable for collaboration with:

nephrology and metabolic analytics teams
critical care research groups
organ support pathway teams
clinical AI safety researchers
digital twin developers
early-warning and escalation platforms

The framework provides explicit representation of:

which regime a patient is leaving
which regime is emerging
how uncertain the transition is
whether stabilization remains structurally feasible

License

MIT

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