# Cross-Regime Transfer Matrix

The Clarus Clinical Stability Benchmark includes cross-regime transfer tests.

These tests evaluate whether models learn general stability reasoning rather than dataset-specific correlations.

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# Transfer Matrix

| Train Dataset | Test Dataset | Transfer Question |
|---|---|---|
| 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

For each row:

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

The Transfer Stability Score is:

TSS = mean F1 across all transfer tests

High TSS suggests the model learned stability reasoning.

Low TSS suggests the model learned dataset-specific surface patterns.

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# Structural Note

Cross-regime transfer is the strongest test in the Clarus benchmark.

Single-dataset performance can be achieved through local pattern learning.

Transfer performance requires models to detect shared stability geometry across different systems.