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README.md

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----
-license: mit
----
+---
+language:
+  - en
+license: mit
+task_categories:
+  - text-classification
+tags:
+  - clinical-trials
+  - clarus
+  - five-node-cascade
+  - trajectory-aware
+  - intervention-pathway
+  - shock
+  - boundary
+size_categories:
+  - 1K<n<10K
+pretty_name: Clarus v0.6 Clinical Five-Node Shock Cascade Intervention Pathway Dataset
+---
+
+# What this repo does
+
+This repository contains a Clarus v0.6 intervention pathway dataset focused on shock cascade boundary dynamics.
+
+The dataset evaluates whether a model can determine if a proposed intervention meaningfully stabilizes a deteriorating shock system represented as a five-node cascade.
+
+The task requires reasoning from:
+
+- multi-node system state
+- trajectory toward instability
+- boundary geometry
+- recovery geometry
+- intervention vector
+- projected trajectory consequence
+
+The model cannot read the answer directly.
+
+It must infer stabilization from the structure of the case.
+
+This shifts the benchmark from simple shock cascade boundary detection to intervention reasoning across a coupled five-node system.
+
+# Core five-node cascade
+
+The shock cascade boundary system is represented using five normalized variables.
+
+- shock_pressure
+- physiological_buffer
+- intervention_lag
+- organ_coupling
+- hemodynamic_instability
+
+These variables capture the main structural drivers of shock escalation and collapse propagation.
+
+# Clinical variable mapping
+
+The normalized cascade variables correspond to measurable clinical signals.
+
+| Cascade Variable | Clinical Measurements | Typical Indicators |
+|------------------|----------------------|-------------------|
+| shock_pressure | Lactate trend<br>Perfusion deficit burden<br>Shock index rise<br>Progressive tissue hypoperfusion | Lactate rising<br>Shock index elevated<br>Poor capillary refill |
+| physiological_buffer | Albumin status<br>Cardiopulmonary reserve<br>Renal reserve<br>Frailty-adjusted resilience | Low reserve<br>Comorbidity burden<br>Poor compensatory tolerance |
+| intervention_lag | Delay to fluids<br>Delay to vasopressors<br>Delay to hemorrhage or source control<br>Delay to organ-support escalation | Late fluids<br>Slow pressor escalation<br>Delayed control |
+| organ_coupling | Cardiovascular-renal interaction<br>Respiratory-circulatory coupling<br>Inflammatory cross-organ spread<br>SOFA-linked propagation | Oliguria with hypotension<br>Respiratory stress plus shock |
+| hemodynamic_instability | MAP decline<br>Vasopressor burden<br>Pulse pressure collapse<br>Perfusion failure burden | MAP < 65<br>Escalating pressors<br>Cold peripheries |
+
+These measurements illustrate how normalized values in the dataset map to real shock cascade physiology.
+
+# Prediction target
+
+The target column is:
+
+`label_shock_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`
+
+and
+
+`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:
+
+- five-node shock state
+- trajectory geometry
+- perturbation geometry
+- recovery geometry
+- intervention vector
+- projected trajectory consequence
+
+Train rows include:
+
+- `stabilization_success`
+- `label_shock_stabilization`
+
+Tester rows exclude these fields.
+
+# Why tester rows exclude stabilization_success
+
+The tester file withholds:
+
+- `stabilization_success`
+- `label_shock_stabilization`
+
+This prevents answer leakage.
+
+The model must infer stabilization using:
+
+- the starting five-node shock 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 shock five-node cascade.
+
+`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`
+- `shock_pressure`
+- `physiological_buffer`
+- `intervention_lag`
+- `organ_coupling`
+- `hemodynamic_instability`
+- `drift_gradient`
+- `drift_velocity`
+- `drift_acceleration`
+- `boundary_distance`
+- `perturbation_radius`
+- `collapse_trigger`
+- `recovery_distance`
+- `recovery_gradient`
+- `return_feasibility`
+- `delta_shock_pressure`
+- `delta_physiological_buffer`
+- `delta_intervention_lag`
+- `delta_organ_coupling`
+- `delta_hemodynamic_instability`
+- `trajectory_shift`
+- `minimal_intervention_path`
+- `stabilization_success`
+- `label_shock_stabilization`
+
+## tester.csv columns
+
+- `scenario_id`
+- `shock_pressure`
+- `physiological_buffer`
+- `intervention_lag`
+- `organ_coupling`
+- `hemodynamic_instability`
+- `drift_gradient`
+- `drift_velocity`
+- `drift_acceleration`
+- `boundary_distance`
+- `perturbation_radius`
+- `collapse_trigger`
+- `recovery_distance`
+- `recovery_gradient`
+- `return_feasibility`
+- `delta_shock_pressure`
+- `delta_physiological_buffer`
+- `delta_intervention_lag`
+- `delta_organ_coupling`
+- `delta_hemodynamic_instability`
+- `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
+
+The five-node cascade format extends this stack from quad structure to higher-order coupled instability modeling.
+
+Version 0.6 evaluates whether a proposed intervention meaningfully alters the trajectory of a five-node shock system approaching collapse.
+
+This marks the transition from cascade monitoring to coupled control reasoning.
+
+# Production deployment
+
+This dataset structure can support clinical decision environments where shock cascade boundary breach must be detected and corrected before irreversible collapse occurs.
+
+Example settings include:
+
+- emergency shock triage
+- ICU shock escalation review
+- hemorrhagic or distributive shock monitoring
+- vasopressor and fluids step-up surveillance
+- multi-organ collapse prevention
+
+# Enterprise and research collaboration
+
+This dataset class supports benchmarking for:
+
+- intervention-aware clinical AI
+- five-node shock 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