ClarusC64/clinical-quad-metabolic-stress-buffer-lag-organ-coupling-mof-transition-v0.7

What this repo does

This repository contains a Clarus v0.7 dataset modeling MOF transition using a quad-coupling system representation.

The dataset extends the v0.6 intervention layer by introducing uncertainty geometry.

The question addressed by earlier versions was:

Can the system be stabilized?

v0.7 adds a second critical question:

How confident are we in that conclusion?

This allows Clarus to distinguish three operational states:

• confident deterioration • confident stabilization • uncertain regime

This distinction matters in multi-organ failure settings where false confidence can delay escalation and obscure evolving systemic collapse.

Core quad

The system state is represented by four structural variables.

• metabolicstress • buffer • lag • organcoupling

These variables capture the structural state of MOF transition rather than isolated measurements.

Clinical variable mapping

This mapping connects the structural quad variables to common MOF indicators.

Prediction target

Target column:

labelmoftransition

Default v0.7 rule:

label = 1 if stabilizationsuccess = 1 AND trajectoryshift < -0.10

Relaxed variant optional rule:

label = 1 if stabilization_success = 1

The stricter rule ensures that a stabilization must produce a meaningful trajectory correction, not only a transient response.

Row structure

Each row represents a multi-organ failure system state and contains:

• system state variables • trajectory dynamics • boundary geometry • uncertainty geometry • perturbation information • recovery dynamics • intervention vector • stabilization outcome • classification label

Uncertainty signals

v0.7 introduces explicit uncertainty modeling.

Two signals are required.

boundaryuncertainty trajectoryuncertainty

An optional support signal may also be present.

regime_confidence

boundary_uncertainty

Normalized variance in the boundary_distance estimate across perturbation samples.

Range: 0–1

Higher values indicate the instability boundary is poorly defined in the current region of the state space.

Low values indicate a stable boundary estimate.

trajectory_uncertainty

Normalized variance in the drift_gradient estimate across consecutive state snapshots.

Range: 0–1

Higher values indicate the direction of system motion is unstable or oscillating.

Low values indicate consistent directional drift.

regime_confidence

Confidence that the system is operating within the assumed MOF transition regime.

Low values indicate ambiguous system behavior or overlapping systemic failure patterns.

Files

data/train.csv

Training rows include:

• intervention vectors • stabilizationsuccess • labelmof_transition

data/tester.csv

Tester rows exclude:

• stabilizationsuccess • labelmof_transition

However the following signals remain available during inference:

• boundaryuncertainty • trajectoryuncertainty • regime_confidence

These signals allow models to estimate prediction reliability.

Example v0.7 row

Example of a high-uncertainty stabilization scenario.

scenarioid: MOF7011 metabolicstress: 0.72 buffer: 0.40 lag: 0.60 organ_coupling: 0.75

driftgradient: 0.57 driftvelocity: 0.50 drift_acceleration: 0.13

boundarydistance: 0.22 boundaryuncertainty: 0.46 trajectoryuncertainty: 0.42 regimeconfidence: 0.49

perturbationradius: 0.26 collapsetrigger: mixedmultiorgansignal

recoverydistance: 0.55 recoverygradient: -0.13 return_feasibility: 0.34

deltametabolicstress: -0.15 deltabuffer: 0.10 deltalag: -0.05 deltaorgancoupling: -0.06

trajectoryshift: -0.12 minimalinterventionpath: organsupport+targeted_escalation

stabilizationsuccess: 1 labelmof_transition: 1

Interpretation:

The intervention succeeds.

But boundary and trajectory uncertainty are both high.

The stabilization result therefore carries low confidence.

This is exactly the type of case v0.7 is designed to expose.

Evaluation

Standard classification metrics:

• accuracy • precision • recall • f1

v0.7 also introduces uncertainty diagnostics.

• recallsuccessfulstabilization • failedrescuerate • highuncertaintyfalsepositiverate • boundarymisconfidencerate • trajectorymisconfidencerate

These metrics detect situations where a model produces confident predictions in uncertain conditions.

Dataset construction

This dataset is generated using the Clarus instability modeling framework.

The generation process follows four stages.

State sampling

System states are sampled across the quad space:

• metabolicstress • buffer • lag • organcoupling

Sampling includes both stable and near-boundary regions.

Trajectory estimation

Trajectory signals are computed for each state:

• driftgradient • driftvelocity • drift_acceleration

These values describe motion through the stability manifold.

Boundary estimation

Instability boundaries are estimated using perturbation sampling.

This produces:

• boundarydistance • boundaryuncertainty

Boundary uncertainty represents the variance of boundary estimates across perturbations.

Intervention simulation

Candidate intervention vectors modify system state:

• deltametabolicstress • deltabuffer • deltalag • deltaorgancoupling

Trajectory is recomputed after intervention to determine:

• trajectoryshift • stabilizationsuccess

Labels are assigned according to the v0.7 rule.

Dataset limitations

This dataset models instability geometry, not complete patient records.

Important limitations:

• Quad variables represent abstract system states rather than full MOF physiology • Intervention effects represent structural transitions rather than full organ-support protocol detail • Uncertainty signals represent estimation uncertainty rather than monitor artifact or sensor noise

The dataset should therefore be interpreted as a system stability benchmark, not a direct clinical decision tool.

Intended use

Appropriate uses include:

• benchmarking instability detection models • studying intervention reasoning in dynamic systems • evaluating uncertainty calibration in safety-critical predictions • training models to recognize unstable stabilization scenarios

Not intended for:

• direct clinical deployment • real-time treatment recommendations • replacing medical judgment

Position in the Clarus dataset ladder

This dataset belongs to the Clarus instability modeling ladder.

The ladder reconstructs the geometry of system failure and recovery.

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 geometry

Relationship to other Clarus datasets

Clarus datasets apply the same instability geometry across domains.

These include:

• clinical systems • financial networks • infrastructure systems • AI coordination environments

Each domain models the same structural signals:

• system state • trajectory dynamics • boundary estimation • recovery pathways • intervention reasoning • uncertainty geometry

This enables transfer of structural reasoning across domains.

Research direction

Future Clarus datasets may extend the ladder through:

• regime transition modeling • multi-cascade coupling • adversarial perturbation environments • stability control policies

These developments move Clarus from a monitoring framework toward a general system stability instrument.

Structural note

Clarus v0.7 extends the intervention ladder.

The instrument now models:

• system state • trajectory • boundary • recovery • intervention • uncertainty

This transforms Clarus from a collapse detector into a calibrated system navigation instrument.

Production deployment

The dataset class is most useful where false confidence carries high operational risk, including:

• early MOF detection • metabolic collapse review • organ-support escalation • unstable multi-organ reassessment • ICU systemic failure monitoring

Enterprise and research collaboration

Potential applications include:

• clinical AI benchmarking • intervention pathway model evaluation • uncertainty calibration research • instability monitoring systems • safety evaluation for clinical decision support models

License

MIT