# Clarus Clinical Stability Benchmark Matrix

The Clarus Clinical Stability Benchmark evaluates whether machine learning models can detect **latent stability dynamics** rather than relying on simple feature correlations.

Each dataset represents a specific **clinical system domain** and **instability mechanism**.

The benchmark also includes **robustness variants** that test whether models remain reliable under incomplete observations and class imbalance.

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# Benchmark Matrix

| Dataset | Domain | Instability Mechanism | Missing Data Variant | Class Imbalance Variant |
|---|---|---|---|---|
| clinical-hemodynamic-collapse | circulation | pressure collapse | ✓ | ✓ |
| clinical-perfusion-instability | circulation | microvascular perfusion failure | ✓ | ✓ |
| clinical-oxygen-transport-instability | circulation | oxygen delivery failure | ✓ | ✓ |
| clinical-respiratory-drive-instability | respiration | ventilatory control failure | ✓ | ✓ |
| clinical-acid-base-instability | physiology | buffering collapse | ✓ | ✓ |
| clinical-renal-filtration-instability | renal | filtration failure | ✓ | ✓ |
| clinical-fluid-balance-instability | renal | volume dysregulation | ✓ | ✓ |
| clinical-electrolyte-instability | renal/metabolic | electrolyte imbalance | ✓ | ✓ |
| clinical-glucose-regulation-instability | metabolic | glucose feedback instability | ✓ | ✓ |
| clinical-nutritional-metabolic-instability | metabolic | metabolic supply failure | ✓ | ✓ |
| clinical-hormonal-feedback-instability | endocrine | endocrine feedback instability | ✓ | ✓ |
| clinical-drug-toxicity-instability | pharmacology | toxic accumulation | ✓ | ✓ |
| clinical-hemostasis-instability | hematology | coagulation imbalance | ✓ | ✓ |
| clinical-microvascular-instability | microcirculation | capillary flow heterogeneity | ✓ | ✓ |
| clinical-neurologic-deterioration-instability | neurology | intracranial perfusion instability | ✓ | ✓ |

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# Robustness Evaluation

The Clarus benchmark includes robustness variants designed to test whether models truly learn stability dynamics.

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## Missing Data Variants

Clinical observations are often incomplete.

To simulate this, trajectory datasets may include variants where observations are missing.

Supported variants:

- **missing t0** — initial observation removed  
- **missing t1** — intermediate observation removed  
- **missing t2** — final observation removed  
- **random missing** — one or more values randomly removed  

These datasets evaluate whether models can infer stability dynamics from **partial trajectories**.

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## Class Imbalance Variants

Many real-world systems exhibit **rare instability events**.

To test robustness to prevalence shifts, datasets may include variants with altered class distributions.

Supported regimes:

- **balanced (50 / 50)**  
- **mild imbalance (70 / 30)**  
- **severe imbalance (90 / 10)**  
- **extreme imbalance (99 / 1)**  

These variants test whether models rely on **true stability reasoning** rather than prevalence heuristics.

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# Benchmark Objective

A robust stability model should remain reliable across:

- multiple instability mechanisms  
- different clinical domains  
- incomplete observations  
- rare-event prevalence conditions  

Models that rely on shallow correlations or class frequency will degrade under these evaluation regimes.

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# Structural Note

Each dataset reflects **latent stability geometry expressed through observable clinical proxies**.

The dataset generator and latent stability rules are not included in the benchmark repositories.