ClarusC64/clinical-quad-oxygen-demand-buffer-lag-coupling-respiratory-collapse-v1.1
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… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-oxygen-demand-buffer-lag-coupling-respiratory-collapse-v1.1.
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
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