| # Cross-Regime Transfer Matrix |
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| The Clarus Clinical Stability Benchmark includes cross-regime transfer tests. |
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| These tests evaluate whether models learn general stability reasoning rather than dataset-specific correlations. |
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| # Transfer Matrix |
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| | Train Dataset | Test Dataset | Transfer Question | |
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| | clinical-hemodynamic-collapse | clinical-perfusion-instability | Does macro-circulatory reasoning transfer to tissue perfusion? | |
| | clinical-perfusion-instability | clinical-microvascular-instability | Does perfusion reasoning transfer to capillary-flow instability? | |
| | clinical-oxygen-transport-instability | clinical-cellular-energy-instability | Does oxygen delivery reasoning transfer to cellular energy failure? | |
| | clinical-respiratory-drive-instability | clinical-acid-base-instability | Does ventilatory control reasoning transfer to acid–base buffering? | |
| | clinical-renal-filtration-instability | clinical-fluid-balance-instability | Does renal filtration reasoning transfer to volume regulation? | |
| | clinical-glucose-regulation-instability | clinical-hormonal-feedback-instability | Does metabolic feedback reasoning transfer to endocrine feedback? | |
| | clinical-immune-cascade-instability | clinical-hemostasis-instability | Does cascade reasoning transfer to coagulation balance? | |
| | clinical-drug-toxicity-instability | clinical-neurologic-deterioration-instability | Does toxic accumulation reasoning transfer to neurologic deterioration? | |
| | clinical-hospital-operational-collapse | clinical-monitoring-failure-instability | Does operational overload reasoning transfer to detection failure? | |
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| # Evaluation Method |
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| For each row: |
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| 1. Train on the train.csv file from the source dataset. |
| 2. Generate predictions for the test.csv file from the target dataset. |
| 3. Evaluate with the target dataset scorer. |
| 4. Record F1, precision, recall, and accuracy. |
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| # Transfer Stability Score |
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| The Transfer Stability Score is: |
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| TSS = mean F1 across all transfer tests |
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| High TSS suggests the model learned stability reasoning. |
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| Low TSS suggests the model learned dataset-specific surface patterns. |
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| # Structural Note |
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| Cross-regime transfer is the strongest test in the Clarus benchmark. |
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| Single-dataset performance can be achieved through local pattern learning. |
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| Transfer performance requires models to detect shared stability geometry across different systems. |
