--- language: - en license: mit task_categories: - text-classification tags: - clinical-trials - clarus - quad-coupling - trajectory-aware - intervention-pathway - trauma size_categories: - 1K 0.9SBP < 90 physiological_buffer Physiological reserveHemoglobin levelPlatelet countCoagulation capacity Hb < 8Platelets < 100KCoagulopathy present intervention_lag Time to hemorrhage controlTime to transfusionDelay to airway stabilizationDelay to surgical intervention Transfusion delay > 30 minDelayed hemorrhage control systemic_coupling Multi-organ coupling stressShock–respiratory interactionInflammatory cascade activationCoagulopathy–bleeding feedback Rising SOFA scoreLactate increaseSystemic inflammatory activation These measurements illustrate how normalized values in the dataset map to real trauma physiology. Prediction target The target column is: label_trauma_stabilization This label indicates whether the intervention pathway produces genuine stabilization. Label logic Default benchmark rule A row is labeled positive only when both conditions hold: stabilization_success = 1 trajectory_shift < -0.10 This rule filters out marginal corrections and ensures that positive examples represent meaningful stabilization. Optional relaxed rule Positive labels may trigger when: stabilization_success = 1 This relaxed rule may be used for exploratory builds but is not the default benchmark configuration. Row structure Each row contains: core trauma state trajectory geometry perturbation geometry recovery geometry intervention vector projected trajectory consequence Train rows include: stabilization_success label_trauma_stabilization Tester rows exclude these fields. Why tester rows exclude stabilization_success The tester file withholds: stabilization_success label_trauma_stabilization This prevents answer leakage. The model must infer stabilization using: the starting trauma state drift toward the failure boundary recovery basin geometry the intervention vector the predicted trajectory consequence This structure forces real intervention reasoning. Minimal intervention path minimal_intervention_path encodes the shortest viable stabilization pathway. Example interpretation: 0 = no viable rescue path 1 = direct stabilization pathway 2 = multi-step stabilization sequence 3 = complex high-risk rescue pathway The field remains visible because the benchmark evaluates whether the model can combine intervention structure with trajectory consequence to determine stabilization. Files data/train.csv — labeled training dataset data/tester.csv — unlabeled benchmark dataset with withheld stabilization signal scorer.py — evaluation metrics and confusion matrix computation cli.py — command-line evaluation wrapper used for benchmark scoring README.md — dataset card and schema documentation Evaluation The scorer reports: accuracy precision recall_successful_stabilization failed_rescue_rate f1 confusion_matrix Primary metric: recall_successful_stabilization Secondary metric: failed_rescue_rate Interpretation: recall_successful_stabilization measures how reliably the model detects interventions that genuinely stabilize the trauma system. failed_rescue_rate measures how often the model fails to recognize a viable stabilization pathway. These metrics prioritize intervention reasoning rather than generic classification accuracy. Schema train.csv columns scenario_id perfusion_pressure physiological_buffer intervention_lag systemic_coupling drift_gradient drift_velocity drift_acceleration boundary_distance perturbation_radius collapse_trigger recovery_distance recovery_gradient return_feasibility delta_perfusion_pressure delta_physiological_buffer delta_intervention_lag delta_systemic_coupling trajectory_shift minimal_intervention_path stabilization_success label_trauma_stabilization tester.csv columns scenario_id perfusion_pressure physiological_buffer intervention_lag systemic_coupling drift_gradient drift_velocity drift_acceleration boundary_distance perturbation_radius collapse_trigger recovery_distance recovery_gradient return_feasibility delta_perfusion_pressure delta_physiological_buffer delta_intervention_lag delta_systemic_coupling trajectory_shift minimal_intervention_path Structural note The Clarus dataset series evolves through progressively richer representations of cascade dynamics. Version progression: v0.1 — cascade state detection v0.2 — trajectory-aware detection v0.3 — dynamic cascade forecasting v0.4 — boundary discovery v0.5 — recovery geometry v0.6 — intervention pathway reasoning Earlier versions identify when instability is developing. Version 0.6 evaluates whether a proposed intervention meaningfully alters the trajectory of a system approaching collapse. This marks the transition from monitoring cascade dynamics to evaluating control pathways. Production deployment This dataset structure can support clinical decision environments where trauma deterioration must be detected and corrected before irreversible transition occurs. Example settings include: emergency trauma intake hemorrhagic shock monitoring surgical escalation planning ICU trauma surveillance multi-organ trauma cascade monitoring Enterprise and research collaboration This dataset class supports benchmarking for: intervention-aware clinical AI trauma cascade modeling recovery feasibility prediction false-stability detection boundary-sensitive decision support systems Contact For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com Instability is detectable. Governance determines whether it propagates. License MIT