| # Clarus Clinical Stability Benchmark Matrix |
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| The Clarus Clinical Stability Benchmark evaluates whether machine learning models can detect **latent stability dynamics** rather than relying on simple feature correlations. |
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| Each dataset represents a specific **clinical system domain** and **instability mechanism**. |
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| The benchmark also includes **robustness variants** that test whether models remain reliable under incomplete observations and class imbalance. |
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| # Benchmark Matrix |
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| | Dataset | Domain | Instability Mechanism | Missing Data Variant | Class Imbalance Variant | |
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| | 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 |
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| The Clarus benchmark includes robustness variants designed to test whether models truly learn stability dynamics. |
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| ## Missing Data Variants |
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| Clinical observations are often incomplete. |
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| To simulate this, trajectory datasets may include variants where observations are missing. |
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| Supported variants: |
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| - **missing t0** — initial observation removed |
| - **missing t1** — intermediate observation removed |
| - **missing t2** — final observation removed |
| - **random missing** — one or more values randomly removed |
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| These datasets evaluate whether models can infer stability dynamics from **partial trajectories**. |
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| ## Class Imbalance Variants |
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| Many real-world systems exhibit **rare instability events**. |
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| To test robustness to prevalence shifts, datasets may include variants with altered class distributions. |
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| Supported regimes: |
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| - **balanced (50 / 50)** |
| - **mild imbalance (70 / 30)** |
| - **severe imbalance (90 / 10)** |
| - **extreme imbalance (99 / 1)** |
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| These variants test whether models rely on **true stability reasoning** rather than prevalence heuristics. |
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| # Benchmark Objective |
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| A robust stability model should remain reliable across: |
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| - multiple instability mechanisms |
| - different clinical domains |
| - incomplete observations |
| - rare-event prevalence conditions |
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| Models that rely on shallow correlations or class frequency will degrade under these evaluation regimes. |
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| # Structural Note |
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| Each dataset reflects **latent stability geometry expressed through observable clinical proxies**. |
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| The dataset generator and latent stability rules are not included in the benchmark repositories. |
