Datasets:

ClarusC64
/

clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v1.1

scenario_id string	infection_load float64	buffer_capacity float64	lag_burden float64	coupling_stress 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	control_sequence_alignment_score float64	control_horizon int64	feedback_response_score float64	intervention_timing_score float64	adaptation_latency int64	control_stability_margin float64	sequence_divergence_margin float64	controller_confidence float64	recovery_consistency_score float64	control_recalibration_count int64	terminal_pathway_state string	feedback_noise_ratio float64	controller_oscillation_score float64	rollback_trigger_count int64	perturbation_radius float64	collapse_trigger string	recovery_distance float64	recovery_gradient float64	return_feasibility float64	optimal_policy_score float64	selected_policy_score float64	counterfactual_outcome_score float64	counterfactual_stability_margin float64	counterfactual_trajectory_shift float64	counterfactual_divergence_time float64	counterfactual_failure_risk float64	policy_regret float64	policy_robustness float64	policy_fragility_index float64	policy_stability_delta float64	policy_confidence_gap float64	deceptive_signal_score float64	local_improvement_score float64	delayed_failure_risk float64	signal_conflict_score float64	short_term_gain_long_term_loss_flag float64	delta_infection_load float64	delta_buffer_capacity float64	delta_lag_burden string	delta_coupling_stress string	trajectory_shift int64	minimal_intervention_path float64	stabilization_success float64	label_sepsis_transition float64
seps001	0.86	0.29	0.69	0.74	0.59	0.77	0.49	0.19	0.3	0.72	0.2	0.23	0.75	0.67	toward_failure	0.41	0.26	0.63	0.84	0.81	0.38	0.34	0.66	antibiotics_then_fluids	source_control_then_antibiotics	0.22	0.4	0.79	4	0.75	0.72	2	0.7	0.43	0.75	0.69	1	partially_stabilized	0.16	0.2	0	0.5	occult_source_persistence	0.39	-0.23	0.54	0.93	0.82	0.85	0.74	-0.16	0.26	0.16	0.1	0.74	0.27	0.24	0.33	0.46	0.37	0	-0.09	0.05	0.03	-0.02	-0.18	antibiotics_then_fluids	1	1	null	null
seps002	0.91	0.21	0.76	0.8	0.65	0.82	0.56	0.14	0.22	0.8	0.26	0.28	0.67	0.74	toward_failure	0.29	0.35	0.69	0.86	0.73	0.27	0.24	0.74	pressor_then_fluids	antibiotics_then_fluids	0.3	0.46	0.65	5	0.6	0.56	4	0.49	0.47	0.66	0.53	3	unstable_recovery	0.23	0.32	2	0.59	refractory_septic_vasoplegia	0.49	-0.12	0.36	0.92	0.58	0.7	0.51	-0.02	0.4	0.34	0.25	0.45	0.45	0.71	0.84	0.79	0.66	1	-0.11	0.03	0.05	-0.03	-0.03	pressor_then_fluids	0	0	null	null
seps003	0.79	0.37	0.58	0.62	0.46	0.63	0.39	0.28	0.39	0.62	0.18	0.19	0.81	0.55	toward_failure	0.49	0.2	0.47	0.77	0.82	0.51	0.4	0.53	antibiotics_then_monitoring	antibiotics_then_fluids	0.17	0.29	0.82	3	0.8	0.77	1	0.76	0.35	0.82	0.79	1	stabilized	0.11	0.14	0	0.35	bacteremia_without_shock	0.32	-0.34	0.67	0.88	0.85	0.82	0.78	-0.23	0.18	0.06	0.84	0.18	0.19	0.23	0.28	0.22	0	-0.05	0.07	-0.02	0.01	-0.24	antibiotics_then_monitoring	1	1	null	null	null
seps004	0.94	0.18	0.82	0.83	0.73	0.88	0.63	0.1	0.18	0.86	0.3	0.32	0.59	0.81	toward_failure	0.22	0.4	0.76	0.88	0.69	0.2	0.17	0.81	mechanical_support_then_source_control	pressor_then_fluids	0.35	0.5	0.57	6	0.51	0.47	5	0.4	0.53	0.61	0.45	4	relapse	0.28	0.37	2	0.68	septic_multiorgan_escalation	0.58	-0.08	0.22	0.96	0.53	0.65	0.43	-0.01	0.43	0.39	0.31	0.42	0.51	0.76	0.87	0.83	0.7	1	-0.12	0.02	0.06	-0.04	-0.02	mechanical_support_then_source_control	0	0	null	null
seps005	0.75	0.43	0.54	0.57	0.39	0.58	0.35	0.32	0.44	0.58	0.15	0.17	0.84	0.49	toward_recovery	0.56	0.18	0.42	0.74	0.84	0.58	0.43	0.47	antibiotics_then_fluids	antibiotics_only	0.14	0.24	0.84	3	0.82	0.79	1	0.78	0.32	0.84	0.81	0	stabilized	0.1	0.12	0	0.32	early_source_control	0.31	-0.36	0.7	0.87	0.84	0.81	0.8	-0.24	0.16	0.05	0.86	0.17	0.16	0.21	0.26	0.2	0	-0.04	0.08	-0.01	0	-0.25	antibiotics_then_fluids	1	1	null	null	null
seps006	0.84	0.27	0.67	0.71	0.55	0.74	0.46	0.21	0.31	0.71	0.21	0.23	0.74	0.64	toward_failure	0.42	0.25	0.61	0.82	0.78	0.37	0.33	0.65	source_control_then_antibiotics	antibiotics_then_fluids	0.23	0.38	0.76	4	0.73	0.7	2	0.68	0.44	0.74	0.67	2	unstable_recovery	0.17	0.22	1	0.51	delayed_source_control	0.41	-0.2	0.49	0.9	0.68	0.77	0.6	-0.06	0.34	0.23	0.15	0.6	0.32	0.6	0.78	0.75	0.58	1	-0.08	0.05	0.04	-0.02	-0.06	source_control_then_antibiotics	0	0	null	null
seps007	0.72	0.46	0.51	0.55	0.35	0.5	0.3	0.36	0.46	0.55	0.13	0.15	0.86	0.44	toward_recovery	0.59	0.16	0.38	0.72	0.85	0.6	0.44	0.43	antibiotics_only	antibiotics_then_fluids	0.12	0.22	0.85	2	0.83	0.8	1	0.79	0.3	0.85	0.82	0	stabilized	0.09	0.11	0	0.3	contained_bacteremia	0.3	-0.38	0.72	0.86	0.83	0.8	0.81	-0.25	0.15	0.04	0.87	0.16	0.13	0.18	0.22	0.18	0	-0.04	0.08	-0.01	0	-0.26	antibiotics_only	1	1	null	null	null
seps008	0.88	0.23	0.74	0.78	0.61	0.81	0.54	0.16	0.24	0.79	0.25	0.27	0.68	0.72	toward_failure	0.31	0.33	0.68	0.84	0.74	0.29	0.25	0.73	pressor_then_fluids_then_antibiotics	antibiotics_then_fluids	0.29	0.44	0.66	5	0.61	0.57	4	0.5	0.46	0.67	0.54	3	relapse	0.22	0.31	2	0.6	vasoplegic_sepsis	0.48	-0.13	0.37	0.91	0.59	0.71	0.52	-0.02	0.39	0.33	0.26	0.44	0.46	0.69	0.83	0.78	0.65	1	-0.1	0.03	0.05	-0.03	-0.03	pressor_then_fluids_then_antibiotics	0	0	null	null
seps009	0.77	0.4	0.56	0.6	0.42	0.6	0.36	0.3	0.41	0.6	0.16	0.18	0.83	0.51	toward_recovery	0.53	0.18	0.44	0.75	0.83	0.56	0.41	0.49	antibiotics_then_fluids	source_control_then_antibiotics	0.15	0.26	0.83	3	0.81	0.78	1	0.77	0.33	0.83	0.8	1	stabilized	0.1	0.13	0	0.33	mixed_inflammatory_shift	0.33	-0.33	0.68	0.89	0.86	0.83	0.79	-0.22	0.17	0.06	0.85	0.17	0.18	0.22	0.27	0.21	0	-0.05	0.07	-0.02	0.01	-0.23	antibiotics_then_fluids	1	1	null	null	null
seps010	0.85	0.26	0.68	0.72	0.57	0.76	0.48	0.2	0.29	0.73	0.22	0.24	0.73	0.66	toward_failure	0.39	0.27	0.62	0.83	0.76	0.35	0.32	0.67	source_control_then_fluids	antibiotics_then_fluids	0.24	0.39	0.72	5	0.68	0.64	3	0.6	0.43	0.71	0.61	2	unstable_recovery	0.18	0.25	1	0.54	drainage_delay	0.44	-0.18	0.45	0.9	0.65	0.75	0.58	-0.05	0.35	0.24	0.18	0.57	0.35	0.62	0.79	0.75	0.6	1	-0.09	0.04	0.04	-0.02	-0.05	source_control_then_fluids	0	0	null	null

Clinical Quad Infection Buffer Lag Coupling Sepsis Transition v1.1

What this repo does

This dataset evaluates whether a model can select the correct control policy when:

multiple sepsis interventions appear viable
early signals suggest improvement
alternative policies produce better long-term outcomes

The task is not prediction.

The task is selecting the correct action under uncertainty, feedback, and misleading signal structure.

Core quad

The system is defined by four interacting variables.

infection_load
buffer_capacity
lag_burden
coupling_stress

These variables define system state.

All signals describe how this state evolves under pressure and intervention.

Clinical variable mapping

Quad Variable	Clinical Measurements	Typical Indicators
infection_load	infectious burden, source severity, microbial pressure	source persistence, bacteremia load, uncontrolled infection
buffer_capacity	physiological reserve, perfusion reserve, immune reserve	remaining compensation, reserve to tolerate septic stress
lag_burden	delayed correction load, unresolved instability debt	late antibiotics, delayed source control, untreated progression
coupling_stress	cross-system destabilization linking infection, perfusion, and organs	vasoplegia, inflammatory spillover, organ strain

Prediction target

label_sepsis_transition

Binary classification.

1 = optimal robust control policy selected
0 = suboptimal or deceptive policy selected

Label logic

label = 1 if:
    stabilization_success = 1
    AND trajectory_shift < -0.10
    AND intervention_alignment_score >= 0.60
    AND control_sequence_alignment_score >= 0.60
    AND recovery_consistency_score >= 0.60
    AND policy_regret <= 0.10
    AND policy_robustness >= 0.60
    AND counterfactual_failure_risk <= 0.30
    AND deceptive_signal_score <= 0.50
What v1.1 adds

Earlier versions answer:

where the system is

where it is moving

which intervention may help

whether a control sequence stabilizes it

v1.1 adds:

is this the best available policy

This introduces:

counterfactual comparison

robustness under perturbation

resistance to deceptive signals

Example scenario (realistic numeric row)
scenario_id: seps002

infection_load: 0.91
buffer_capacity: 0.21
lag_burden: 0.76
coupling_stress: 0.80

drift_gradient: 0.65
drift_velocity: 0.82
trajectory_shift: -0.03

intervention_alignment_score: 0.73
control_sequence_alignment_score: 0.65
recovery_consistency_score: 0.53

optimal_policy_score: 0.92
selected_policy_score: 0.58

policy_regret: 0.34
policy_robustness: 0.45
policy_stability_delta: 0.25

local_improvement_score: 0.84
delayed_failure_risk: 0.79
deceptive_signal_score: 0.71
signal_conflict_score: 0.66

short_term_gain_long_term_loss_flag: 1

stabilization_success: 0
label_sepsis_transition: 0

Interpretation:

the intervention produces early improvement

alignment scores appear acceptable

long-term septic control is weak

a superior counterfactual policy exists

deceptive signals mask later collapse

The correct decision is to reject this policy.

Row structure

Each row includes:

system state

trajectory and boundary signals

intervention candidates

control sequence behavior

counterfactual comparisons

policy diagnostics

adversarial signals

outcome fields

Dataset construction

Scenarios are generated by:

sampling septic system states across the quad

generating multiple intervention pathways

simulating outcome trajectories

For each scenario:

optimal_policy_score is assigned from the best trajectory

selected_policy_score is assigned from a candidate path

Derived signals:

policy_regret = optimal − selected

policy_robustness = stability under perturbation

policy_stability_delta = terminal outcome difference

Adversarial structure is introduced by:

high short-term improvement

delayed instability

conflicting subsystem signals

misleading alignment patterns

Counterfactual fields are computed during construction.

Files

data/train.csv
Full dataset with labels

data/tester.csv
Same schema without:

stabilization_success

label_sepsis_transition

scorer.py
Evaluation script

benchmark_spec.json
Formal benchmark definition

dataset_schema.json
Full schema with types and ranges

README.md
This file

Evaluation

Primary metric:

recall_optimal_policy_selection

Secondary metric:

false_robust_policy_rate

Additional diagnostics:

policy_regret_error

policy_robustness_error

policy_stability_delta_error

counterfactual_miss_rate

deceptive_policy_selection_rate

control_sequence_alignment_accuracy

Running the scorer
python scorer.py data/train.csv predictions.csv
Dataset limitations

deceptive_signal_score reflects structural scenario design, not adversarial model input

counterfactual policies are constructed, not exhaustively searched

robustness is simulated

clinical abstraction may omit domain-specific nuance

Intended use

Use for:

benchmarking control decision systems

evaluating policy selection

stress testing models under uncertainty

Not for:

direct clinical decision making

patient-level diagnosis

deployment without validation

standalone decision systems

Structural note

Each version adds one capability:

v0.x: detection and trajectory

v0.6–v0.9: intervention and competition

v1.0: control

v1.1: counterfactual and adversarial evaluation

v1.1 introduces:

multiple valid actions

only one optimal

failure modes that look correct

Production deployment

Applicable to:

clinical systems

infrastructure control

autonomous systems

financial risk

Enterprise and research collaboration

Clarus evaluates system stability.

The focus is:

not what happens next

but whether the chosen action stabilizes the system

License

MIT

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