--- language: en license: mit task_categories: - text-classification tags: - clinical-trials - five-node-cascade - closed-loop-control - mof-boundary - clarus-v1.0 size_categories: - 1K= 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 shows why v1.0 rewards true closed-loop cascade control, not temporary improvement alone. ## Row structure Each row represents a multi-organ-failure cascade boundary scenario with: - five cascade nodes - 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 ### Five-node cascade variables - metabolic_stress - perfusion_deficit - renal_strain - hepatic_drift - buffer_capacity ### 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_metabolic_stress - delta_perfusion_deficit - delta_renal_strain - delta_hepatic_drift - delta_buffer_capacity ## Dataset construction Each scenario is generated using a structured simulation of five-node multi-organ-failure cascade deterioration and intervention sequences. ### 1. Cascade initialization A baseline multi-organ-failure cascade state is sampled across the five nodes. ### 2. Instability evolution The cascade evolves using trajectory signals that determine movement toward collapse or recovery boundaries. ### 3. Intervention competition Candidate rescue sequences are evaluated using intervention competition geometry. ### 4. Closed-loop control execution A selected rescue path is applied through a sequence of actions. Control signals measure: - sequence alignment - timing - feedback interpretation - adaptation speed - durability of recovery across the cascade The final state determines: - `stabilization_success` - `terminal_pathway_state` - `label_mof_boundary` ## 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 rescue path is best?** v1.0 asks a harder question: **Can the controller stay aligned with reality while a five-node multi-organ-failure cascade 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 multi-organ-failure cascades The benchmark evaluates control reasoning, not medical safety. ## Intended use This dataset is intended for research on: - instability prediction - sequential decision reasoning - closed-loop cascade 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 across a five-node cascade. 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: - multi-organ-failure cascade stabilization - ICU escalation pathway planning - multistage rescue benchmarking - 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