Datasets:

ClarusC64
/

clinical-quad-oxygen-demand-buffer-lag-coupling-respiratory-collapse-v1.0

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	delta_oxygen_demand float64	delta_buffer_capacity float64	delta_lag_burden float64	delta_coupling_stress float64	trajectory_shift float64	minimal_intervention_path string	stabilization_success int64	label_respiratory_collapse int64
resp001	0.86	0.28	0.73	0.79	0.67	0.6	0.21	0.18	0.34	0.7	0.17	0.23	0.82	0.78	toward_failure	0.42	0.2	0.55	0.82	0.77	0.41	0.25	0.62	oxygen_then_noninvasive_ventilation	bronchodilator_then_oxygen	0.18	0.15	0.8	3	0.75	0.79	1	0.72	0.23	0.81	0.77	1	stabilized	0.11	0.1	0	0.42	desaturation_spike	0.3	-0.4	0.75	-0.18	0.21	-0.15	-0.18	-0.22	oxygen_then_noninvasive_ventilation	1	1
resp002	0.91	0.22	0.8	0.86	0.75	0.68	0.27	0.12	0.28	0.79	0.23	0.28	0.78	0.85	toward_failure	0.36	0.24	0.62	0.79	0.69	0.3	0.2	0.67	intubation_then_peep_escalation	oxygen_then_noninvasive_ventilation	0.27	0.24	0.55	1	0.5	0.54	3	0.35	0.19	0.65	0.43	3	unstable_recovery	0.17	0.3	1	0.57	hypercapnic_fatigue	0.46	-0.17	0.4	-0.08	0.05	-0.04	-0.06	-0.05	intubation_then_peep_escalation	0	0
resp003	0.78	0.36	0.62	0.68	0.5	0.44	0.13	0.28	0.43	0.56	0.15	0.18	0.85	0.65	toward_failure	0.49	0.16	0.4	0.74	0.7	0.54	0.17	0.56	bronchodilator_then_oxygen	oxygen_then_monitoring	0.14	0.1	0.74	2	0.75	0.76	1	0.69	0.16	0.78	0.73	1	partially_stabilized	0.08	0.12	0	0.34	bronchospasm_drift	0.27	-0.3	0.71	-0.13	0.16	-0.1	-0.12	-0.14	bronchodilator_then_oxygen	1	1
resp004	0.94	0.19	0.84	0.89	0.81	0.73	0.3	0.09	0.24	0.86	0.27	0.32	0.75	0.91	toward_failure	0.3	0.27	0.7	0.76	0.58	0.22	0.15	0.72	intubation_then_proning	recruitment_then_peep_escalation	0.33	0.3	0.44	0	0.39	0.41	4	0.24	0.14	0.58	0.31	4	relapse	0.22	0.38	2	0.65	refractory_ards_transition	0.55	-0.1	0.25	-0.03	0.02	-0.02	-0.03	-0.03	intubation_then_proning	0	0
resp005	0.82	0.32	0.68	0.74	0.58	0.51	0.16	0.23	0.39	0.62	0.16	0.21	0.84	0.72	toward_failure	0.45	0.18	0.47	0.8	0.75	0.47	0.22	0.6	oxygen_then_noninvasive_ventilation	bronchodilator_then_oxygen	0.17	0.13	0.77	2	0.79	0.78	1	0.72	0.2	0.8	0.77	1	stabilized	0.1	0.09	0	0.4	postop_atelectatic_shift	0.32	-0.34	0.74	-0.15	0.19	-0.13	-0.16	-0.17	oxygen_then_noninvasive_ventilation	1	1
resp006	0.87	0.27	0.76	0.81	0.69	0.62	0.23	0.16	0.31	0.74	0.2	0.25	0.81	0.8	toward_failure	0.39	0.22	0.57	0.81	0.65	0.33	0.19	0.64	recruitment_then_peep_escalation	oxygen_then_noninvasive_ventilation	0.24	0.2	0.62	1	0.58	0.61	2	0.49	0.19	0.69	0.59	2	unstable_recovery	0.15	0.26	1	0.49	alveolar_collapse_persistence	0.41	-0.21	0.46	-0.07	0.07	-0.05	-0.08	-0.07	recruitment_then_peep_escalation	1	0
resp007	0.75	0.4	0.57	0.61	0.42	0.36	0.1	0.34	0.49	0.5	0.12	0.16	0.87	0.57	toward_failure	0.53	0.14	0.33	0.7	0.67	0.58	0.16	0.54	oxygen_then_monitoring	bronchodilator_then_oxygen	0.13	0.09	0.71	2	0.72	0.74	1	0.67	0.15	0.76	0.7	1	partially_stabilized	0.08	0.12	0	0.3	transient_wheeze_instability	0.26	-0.26	0.68	-0.12	0.14	-0.09	-0.11	-0.13	oxygen_then_monitoring	1	1
resp008	0.9	0.23	0.79	0.85	0.77	0.7	0.28	0.11	0.26	0.82	0.25	0.29	0.77	0.88	toward_failure	0.34	0.25	0.65	0.76	0.54	0.26	0.16	0.7	intubation_then_proning	intubation_then_peep_escalation	0.31	0.28	0.45	0	0.4	0.4	4	0.23	0.14	0.55	0.3	4	irreversible_collapse	0.23	0.41	2	0.67	refractory_hypoxemia	0.6	-0.08	0.2	-0.02	0.01	-0.01	-0.02	-0.03	intubation_then_proning	0	0
resp009	0.79	0.35	0.65	0.7	0.53	0.47	0.15	0.25	0.41	0.58	0.15	0.19	0.85	0.68	toward_failure	0.47	0.17	0.44	0.79	0.73	0.49	0.2	0.58	oxygen_then_noninvasive_ventilation	recruitment_then_peep_escalation	0.17	0.12	0.75	3	0.76	0.77	1	0.7	0.18	0.79	0.74	1	stabilized	0.09	0.1	0	0.37	mixed_vq_shift	0.29	-0.32	0.73	-0.14	0.18	-0.12	-0.14	-0.15	oxygen_then_noninvasive_ventilation	1	1

ClarusC64/clinical-quad-oxygen-demand-buffer-lag-coupling-respiratory-collapse-v1.0

What this repo does

This repository provides a Clarus v1.0 benchmark for respiratory collapse under a four-variable clinical quad:

oxygen_demand
buffer_capacity
lag_burden
coupling_stress

The v1.0 upgrade is Closed-Loop Control Geometry.

The task is no longer limited to detecting deterioration or ranking one intervention against another.

It tests whether a controller can:

choose the right path
apply the path in the right sequence
read feedback from the system
adapt in time
maintain durable recovery

Concept ladder

Version	Capability
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
v1.0	closed-loop control geometry

Core quad

oxygen_demand

Total respiratory and metabolic demand pressing the system toward oxygen failure.

buffer_capacity

Remaining reserve available to absorb respiratory strain.

lag_burden

Delayed correction pressure caused by unresolved respiratory instability.

coupling_stress

Cross-system destabilization linking lungs, perfusion, metabolism, and hemodynamics.

Clinical variable mapping

Variable	Clinical interpretation	Typical measurement proxies
oxygen_demand	respiratory and metabolic burden	work of breathing, oxygen extraction, respiratory rate
buffer_capacity	remaining reserve	oxygen delivery margin, ventilatory reserve, compensatory reserve
lag_burden	unresolved instability debt	delayed airway support, persistent hypoxemia, unresolved fatigue
coupling_stress	cross-organ destabilization	V/Q mismatch, hemodynamic strain, metabolic spillover

These four variables define the structural state of the respiratory system rather than a single physiological measurement.

Prediction target

The target is label_respiratory_collapse.

Default stronger v1.0 rule

label = 1 if all of the following hold:

stabilization_success = 1
trajectory_shift < -0.10
intervention_alignment_score >= 0.60
control_sequence_alignment_score >= 0.60
recovery_consistency_score >= 0.60

Mid-strength variant

label = 1 if:

stabilization_success = 1
trajectory_shift < -0.10
control_sequence_alignment_score >= 0.60

Relaxed variant

label = 1 if stabilization_success = 1

Example row

The following simplified row shows a near-miss under the strict v1.0 label rule.

Field	Value
stabilization_success	1
trajectory_shift	-0.11
intervention_alignment_score	0.64
control_sequence_alignment_score	0.59
recovery_consistency_score	0.74

Four of the five conditions are satisfied.

But control_sequence_alignment_score = 0.59 falls below the threshold of 0.60.

Therefore:

label = 0

This illustrates why v1.0 measures true closed-loop control quality, not temporary improvement alone.

Row structure

Each row represents a respiratory deterioration scenario with:

quad state variables
trajectory signals
boundary geometry
regime transition signals
intervention competition signals
closed-loop control signals
perturbation and recovery signals
delta signals
intervention path and final label

Signal groups

Quad state variables

oxygen_demand
buffer_capacity
lag_burden
coupling_stress

Trajectory signals

drift_gradient
drift_velocity
drift_acceleration
trajectory_shift

Boundary geometry

boundary_distance
secondary_boundary_distance
boundary_competition_ratio

Uncertainty signals

boundary_uncertainty
trajectory_uncertainty
regime_confidence
transition_uncertainty
intervention_uncertainty
controller_confidence
feedback_noise_ratio

Regime transition signals

regime_transition_score
transition_direction
regime_separation_margin
transition_velocity

Intervention signals

intervention_leverage_score
intervention_alignment_score
rescue_window_width
pathway_divergence_margin
intervention_competition_ratio
primary_intervention_path
secondary_intervention_path
pathway_switch_velocity
minimal_intervention_path

Closed-loop control signals (v1.0)

control_sequence_alignment_score
control_horizon
feedback_response_score
intervention_timing_score
adaptation_latency
control_stability_margin
sequence_divergence_margin
controller_confidence
recovery_consistency_score
control_recalibration_count
terminal_pathway_state

Possible terminal pathway states include:

stabilized
partially_stabilized
unstable_recovery
relapse
irreversible_collapse

Optional control diagnostics included

feedback_noise_ratio
controller_oscillation_score
rollback_trigger_count

Recovery signals

recovery_distance
recovery_gradient
return_feasibility

Perturbation signals

perturbation_radius
collapse_trigger

Delta signals

delta_oxygen_demand
delta_buffer_capacity
delta_lag_burden
delta_coupling_stress

Dataset construction

Each scenario is generated using a structured simulation of respiratory deterioration and intervention sequences.

1. System initialization

A baseline respiratory state is sampled across the quad variables.

2. Instability evolution

The system evolves using trajectory signals that determine movement toward deterioration or recovery boundaries.

3. Intervention competition

Candidate interventions are evaluated using intervention competition geometry.

4. Closed-loop control execution

A selected control path is applied through a sequence of actions.

Control signals measure:

sequence alignment
timing
feedback interpretation
adaptation speed
durability of recovery

The final state determines:

stabilization_success
terminal_pathway_state
label_respiratory_collapse

Files

data/train.csv
Labeled training set with the full v1.0 schema.
data/tester.csv
Test-style file. stabilization_success is withheld.
scorer.py
Reference scorer for binary metrics and v1.0 control diagnostics.
benchmark_spec.json
Canonical machine-readable benchmark spec.
dataset_schema.json
Machine-readable structural schema with column groups, types, ranges, and row order.

Evaluation

Primary metric

recall_correct_control_sequence_selection

Secondary metric

false_effective_control_rate

Binary metrics

accuracy
precision
recall
f1
confusion matrix

Closed-loop diagnostics

primary_intervention_path_accuracy
secondary_intervention_path_accuracy
control_sequence_alignment_accuracy
control_horizon_error
feedback_response_accuracy
intervention_timing_accuracy
high_uncertainty_control_miss_rate
narrow_window_control_miss_rate
adaptation_latency_error
control_stability_error
recovery_consistency_error
recalibration_overuse_rate
controller_oscillation_misread_rate
terminal_pathway_state_accuracy

Structural interpretation

Earlier Clarus datasets asked:

Which intervention is best?

v1.0 asks a harder question:

Can the controller stay aligned with reality while the system evolves?

Real systems fail not only because the first action is wrong.

They also fail because:

feedback is misread
adaptation is delayed
interventions are mistimed
control oscillations destabilize recovery

v1.0 measures these failure modes directly.

Dataset limitations

This dataset models structural control dynamics, not detailed clinical treatment protocols.

Important limitations:

intervention paths are simplified abstractions
control signals represent structural decision quality, not pharmacological precision
physiological variables are normalized system indicators rather than raw bedside measurements
the dataset does not capture the full biological variability of real respiratory collapse

The benchmark evaluates control reasoning, not medical safety.

Intended use

This dataset is intended for research on:

instability prediction
sequential decision reasoning
closed-loop control modeling
intervention planning under uncertainty
AI robustness in dynamic clinical-like environments

Not intended for

This dataset must not be used for:

real clinical decision support
medical diagnosis
treatment recommendation systems
automated ICU control systems
deployment in patient care environments

Structural note

This v1.0 dataset marks the move from intervention competition to actual control logic.

The benchmark asks whether the controller stays aligned with reality across time.

That is the threshold where Clarus becomes a control-layer instrument rather than only a detection or ranking layer.

Production deployment

This dataset format is suitable for controlled benchmarking in domains where sequential intervention quality matters more than one-shot classification.

Examples include:

respiratory failure stabilization
oxygenation collapse monitoring
ventilatory escalation planning
distributed system control
multi-step recovery planning

Enterprise and research collaboration

This repo is part of the broader Clarus ladder for modeling instability, recovery, and control under feedback.

It is designed for:

benchmark development
model evaluation
intervention policy testing
control-sequence auditing
future cross-domain transfer into other high-stakes systems

License

MIT

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