Update README.md

README.md

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----
-license: mit
----
+---
+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<n<10K
+pretty_name: Clinical Five Node MOF Cascade Boundary v1.0
+---
+
+# ClarusC64/clinical-five-node-mof-cascade-boundary-v1.0
+
+## What this repo does
+
+This repository provides a Clarus v1.0 benchmark for multi-organ-failure cascade boundary dynamics using a five-node clinical cascade:
+
+- metabolic_stress
+- perfusion_deficit
+- renal_strain
+- hepatic_drift
+- buffer_capacity
+
+The v1.0 upgrade is Closed-Loop Control Geometry.
+
+The task is no longer limited to detecting deterioration, forecasting cascade spread, or ranking one intervention against another.
+
+It tests whether a controller can:
+
+- choose the right cascade rescue path
+- apply that path in the right sequence
+- read feedback from the system
+- adapt in time
+- maintain durable recovery across the cascade
+
+## 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 five-node cascade
+
+### metabolic_stress
+
+Primary metabolic burden driving multi-organ-failure escalation.
+
+### perfusion_deficit
+
+Circulatory under-delivery and tissue perfusion failure.
+
+### renal_strain
+
+Renal stress and clearance instability within the cascade.
+
+### hepatic_drift
+
+Hepatic destabilization and downstream metabolic drift.
+
+### buffer_capacity
+
+Remaining reserve available to absorb cascade stress.
+
+## Clinical variable mapping
+
+| Variable | Clinical interpretation | Typical measurement proxies |
+|---|---|---|
+| metabolic_stress | metabolic burden | lactate load, acidosis burden, oxygen debt |
+| perfusion_deficit | tissue under-delivery | MAP instability, capillary refill deficit, peripheral shutdown |
+| renal_strain | renal destabilization | creatinine rise, urine drop, clearance burden |
+| hepatic_drift | hepatic deterioration | bilirubin drift, transaminase strain, synthetic stress |
+| buffer_capacity | remaining reserve | perfusion reserve, metabolic reserve, organ tolerance |
+
+These five variables define the structural state of the multi-organ-failure cascade rather than isolated bedside readings.
+
+## Prediction target
+
+The target is `label_mof_boundary`.
+
+### 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 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