Datasets:
scenario_id stringclasses 10
values | pressure float64 0.48 0.78 | buffer_capacity float64 0.43 0.74 | coupling_strength float64 0.38 0.66 | trajectory_drift float64 -0.05 0.23 | intervention_A_effect float64 0.29 0.62 | intervention_B_effect float64 0.34 0.7 | intervention_C_effect float64 0.28 0.76 | label_best_intervention stringclasses 3
values |
|---|---|---|---|---|---|---|---|---|
cic_train_001 | 0.48 | 0.74 | 0.38 | -0.05 | 0.61 | 0.34 | 0.28 | A |
cic_train_002 | 0.52 | 0.7 | 0.41 | -0.02 | 0.42 | 0.66 | 0.39 | B |
cic_train_003 | 0.57 | 0.65 | 0.45 | 0.03 | 0.31 | 0.49 | 0.71 | C |
cic_train_004 | 0.6 | 0.61 | 0.48 | 0.05 | 0.55 | 0.37 | 0.41 | A |
cic_train_005 | 0.63 | 0.58 | 0.51 | 0.08 | 0.39 | 0.68 | 0.44 | B |
cic_train_006 | 0.66 | 0.55 | 0.54 | 0.11 | 0.33 | 0.41 | 0.73 | C |
cic_train_007 | 0.69 | 0.52 | 0.57 | 0.14 | 0.58 | 0.36 | 0.4 | A |
cic_train_008 | 0.72 | 0.49 | 0.6 | 0.17 | 0.41 | 0.7 | 0.45 | B |
cic_train_009 | 0.75 | 0.46 | 0.63 | 0.2 | 0.29 | 0.47 | 0.76 | C |
cic_train_010 | 0.78 | 0.43 | 0.66 | 0.23 | 0.62 | 0.38 | 0.42 | A |
Clinical Intervention Competition Sepsis Detection Overview
This dataset tests whether a model can determine which intervention pathway best stabilizes a sepsis-like clinical system.
In real clinical settings multiple interventions may be available at the same time. Each intervention affects system dynamics differently. Some actions move the system toward recovery while others fail to meaningfully counteract the instability trajectory.
The task is to determine which intervention most effectively stabilizes the system.
Prediction target
label_best_intervention
A = intervention A provides the strongest stabilizing effect B = intervention B provides the strongest stabilizing effect C = intervention C provides the strongest stabilizing effect
The task is to identify which intervention path most effectively counters the system’s instability trajectory.
Row structure
Each row represents a synthetic clinical scenario.
Columns:
scenario_id pressure buffer_capacity coupling_strength trajectory_drift intervention_A_effect intervention_B_effect intervention_C_effect
Training rows include the label. Tester rows omit the label.
Evaluation
The scoring script reports:
accuracy
Primary metric accuracy
Accuracy is used because the task requires selecting the single best intervention pathway among competing options.
Why this benchmark matters
Clinical decision making often involves choosing between several possible treatments.
Different interventions interact with the underlying system dynamics in different ways. An intervention that stabilizes one system state may be ineffective or harmful in another.
This benchmark tests whether models can reason about intervention competition in a dynamical system rather than simply detecting deterioration or intervention presence.
Structural note
This dataset exposes system geometry while keeping the generator used to produce the scenarios private.
The goal is to evaluate whether models can reason about structural intervention competition rather than memorizing treatment patterns.
Clarus Stability Geometry Benchmarks
This dataset is part of a broader benchmark family exploring instability and recovery in complex systems.
Related probes include:
clinical-compensation-collapse-sepsis-v1 clinical-fork-point-sepsis-transition-v1 clinical-organ-failure-cascade-v1 clinical-recovery-window-sepsis-v1 clinical-intervention-alignment-sepsis-v1 clinical-recovery-stability-sepsis-v1 clinical-false-stability-sepsis-v1
Together these benchmarks map the lifecycle of instability and recovery in clinical dynamical systems.
License
MIT
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