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scenario_id
string
oxygen_demand
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_oxygen_demand
float64
delta_buffer_capacity
float64
delta_lag_burden
string
delta_coupling_stress
string
trajectory_shift
int64
minimal_intervention_path
float64
stabilization_success
float64
label_respiratory_collapse
float64
resp001
0.84
0.32
0.68
0.71
0.57
0.74
0.46
0.21
0.33
0.69
0.18
0.21
0.76
0.64
toward_failure
0.43
0.24
0.61
0.82
0.79
0.41
0.35
0.63
oxygen_then_niv
recruitment_then_peep_adjustment
0.21
0.39
0.77
4
0.73
0.71
2
0.69
0.41
0.74
0.68
1
partially_stabilized
0.15
0.19
0
0.48
hypoxemic_escalation
0.38
-0.24
0.55
0.92
0.81
0.84
0.72
-0.17
0.27
0.17
0.11
0.73
0.28
0.22
0.31
0.44
0.36
0
-0.09
0.05
0.03
-0.02
-0.19
oxygen_then_niv
1
1
null
null
resp002
0.89
0.24
0.73
0.77
0.62
0.79
0.54
0.16
0.24
0.78
0.24
0.26
0.68
0.71
toward_failure
0.31
0.33
0.67
0.84
0.72
0.29
0.27
0.72
intubation_then_proning
oxygen_then_niv
0.29
0.44
0.66
5
0.61
0.57
4
0.51
0.46
0.67
0.54
3
unstable_recovery
0.22
0.31
2
0.57
hypercapnic_load
0.47
-0.13
0.38
0.91
0.59
0.71
0.52
-0.03
0.39
0.32
0.23
0.47
0.44
0.69
0.83
0.78
0.65
1
-0.1
0.03
0.05
-0.03
-0.04
intubation_then_proning
0
0
null
null
resp003
0.78
0.39
0.59
0.63
0.43
0.61
0.37
0.29
0.4
0.61
0.17
0.18
0.82
0.52
toward_failure
0.5
0.19
0.46
0.75
0.81
0.52
0.39
0.52
bronchodilator_then_oxygen
oxygen_then_niv
0.16
0.28
0.82
3
0.79
0.76
1
0.75
0.34
0.81
0.78
1
stabilized
0.11
0.15
0
0.34
bronchospasm
0.31
-0.35
0.68
0.87
0.84
0.81
0.77
-0.24
0.19
0.06
0.85
0.18
0.18
0.24
0.29
0.22
0
-0.05
0.07
-0.02
0.01
-0.25
bronchodilator_then_oxygen
1
1
null
null
null
resp004
0.92
0.19
0.81
0.79
0.71
0.86
0.61
0.11
0.19
0.84
0.29
0.31
0.61
0.79
toward_failure
0.24
0.38
0.73
0.86
0.68
0.22
0.18
0.79
ecmo_then_proning
intubation_then_proning
0.34
0.49
0.58
6
0.53
0.49
5
0.42
0.51
0.62
0.47
4
relapse
0.27
0.36
2
0.66
refractory_ards
0.56
-0.09
0.24
0.95
0.54
0.66
0.44
-0.01
0.41
0.38
0.29
0.43
0.5
0.74
0.86
0.81
0.69
1
-0.11
0.02
0.06
-0.04
-0.02
ecmo_then_proning
0
0
null
null
resp005
0.74
0.44
0.55
0.58
0.38
0.57
0.34
0.33
0.45
0.57
0.15
0.16
0.84
0.47
toward_recovery
0.57
0.17
0.41
0.73
0.83
0.59
0.42
0.46
oxygen_then_niv
high_flow_oxygen
0.14
0.24
0.84
3
0.81
0.78
1
0.77
0.32
0.83
0.8
0
stabilized
0.1
0.12
0
0.31
vq_mismatch
0.3
-0.37
0.71
0.86
0.83
0.8
0.79
-0.25
0.17
0.05
0.86
0.17
0.15
0.2
0.25
0.2
0
-0.04
0.08
-0.01
0
-0.26
oxygen_then_niv
1
1
null
null
null
resp006
0.83
0.29
0.66
0.7
0.54
0.73
0.45
0.22
0.31
0.7
0.2
0.22
0.75
0.63
toward_failure
0.42
0.25
0.6
0.81
0.77
0.38
0.34
0.64
recruitment_then_peep_adjustment
oxygen_then_niv
0.22
0.37
0.75
4
0.72
0.69
2
0.67
0.42
0.73
0.66
2
unstable_recovery
0.16
0.21
1
0.5
alveolar_derecruitment
0.4
-0.21
0.5
0.89
0.67
0.76
0.61
-0.07
0.35
0.22
0.14
0.61
0.31
0.58
0.76
0.72
0.57
1
-0.08
0.05
0.04
-0.02
-0.06
recruitment_then_peep_adjustment
0
0
null
null
resp007
0.71
0.46
0.52
0.56
0.34
0.49
0.29
0.37
0.47
0.54
0.13
0.15
0.86
0.43
toward_recovery
0.6
0.16
0.37
0.71
0.84
0.61
0.44
0.42
high_flow_oxygen
oxygen_then_niv
0.12
0.22
0.85
2
0.83
0.8
1
0.79
0.29
0.84
0.82
0
stabilized
0.09
0.11
0
0.29
atelectatic_shift
0.29
-0.39
0.73
0.85
0.82
0.79
0.8
-0.26
0.16
0.04
0.88
0.16
0.12
0.18
0.23
0.18
0
-0.04
0.08
-0.01
0
-0.27
high_flow_oxygen
1
1
null
null
null
resp008
0.87
0.25
0.72
0.75
0.6
0.8
0.53
0.17
0.25
0.77
0.25
0.27
0.69
0.7
toward_failure
0.33
0.32
0.66
0.83
0.73
0.3
0.26
0.71
intubation_then_mode_switch
oxygen_then_niv
0.28
0.43
0.67
5
0.62
0.58
4
0.52
0.45
0.68
0.55
3
relapse
0.21
0.3
2
0.58
ventilator_mismatch
0.46
-0.14
0.39
0.9
0.6
0.7
0.53
-0.02
0.38
0.31
0.25
0.46
0.42
0.67
0.82
0.76
0.64
1
-0.1
0.03
0.05
-0.03
-0.03
intubation_then_mode_switch
0
0
null
null
resp009
0.76
0.41
0.57
0.6
0.41
0.59
0.35
0.31
0.42
0.6
0.16
0.17
0.83
0.5
toward_recovery
0.54
0.18
0.43
0.74
0.82
0.55
0.4
0.49
oxygen_then_niv
bronchodilator_then_oxygen
0.15
0.26
0.83
3
0.8
0.77
1
0.76
0.33
0.82
0.79
1
stabilized
0.1
0.14
0
0.33
mixed_vq_shift
0.32
-0.34
0.67
0.88
0.85
0.82
0.78
-0.23
0.18
0.06
0.84
0.17
0.17
0.22
0.27
0.21
0
-0.05
0.07
-0.02
0.01
-0.24
oxygen_then_niv
1
1
null
null
null
resp010
0.86
0.27
0.7
0.73
0.58
0.78
0.5
0.19
0.28
0.74
0.22
0.24
0.72
0.67
toward_failure
0.37
0.28
0.63
0.82
0.75
0.35
0.31
0.68
intubation_then_proning
oxygen_then_niv
0.25
0.4
0.7
5
0.66
0.63
3
0.59
0.43
0.7
0.6
2
unstable_recovery
0.18
0.25
1
0.54
respiratory_muscle_fatigue
0.43
-0.18
0.46
0.89
0.64
0.74
0.58
-0.05
0.36
0.24
0.18
0.58
0.34
0.6
0.78
0.74
0.59
1
-0.09
0.04
0.04
-0.02
-0.05
intubation_then_proning
0
0
null
null

Clinical Quad Oxygen Demand Buffer Lag Coupling Respiratory Collapse v1.1

What this repo does

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

  • multiple respiratory 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.

  • oxygen_demand
  • 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
oxygen_demand oxygen requirement, FiO2 burden, work of breathing pressure escalating oxygen need, desaturation pressure, rising ventilatory demand
buffer_capacity physiological reserve, lung reserve, hemodynamic tolerance preserved reserve, compensatory space, tolerance to escalation
lag_burden delayed correction load, unresolved instability debt delayed intervention, prolonged hypoxemia, accumulated respiratory debt
coupling_stress cross-system destabilization linking lungs, perfusion, and downstream organs V/Q mismatch spillover, hemodynamic stress, organ strain

Prediction target

label_respiratory_collapse

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: resp002

oxygen_demand: 0.89
buffer_capacity: 0.24
lag_burden: 0.73
coupling_stress: 0.77

drift_gradient: 0.62
drift_velocity: 0.79
trajectory_shift: -0.04

intervention_alignment_score: 0.72
control_sequence_alignment_score: 0.66
recovery_consistency_score: 0.54

optimal_policy_score: 0.91
selected_policy_score: 0.59

policy_regret: 0.32
policy_robustness: 0.47
policy_stability_delta: 0.23

local_improvement_score: 0.83
delayed_failure_risk: 0.78
deceptive_signal_score: 0.69
signal_conflict_score: 0.65

short_term_gain_long_term_loss_flag: 1

stabilization_success: 0
label_respiratory_collapse: 0

Interpretation:

the intervention produces early improvement

alignment scores appear acceptable

long-term respiratory 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 respiratory 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_respiratory_collapse

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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