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Clinical Reasoning Benchmark v0.2
This benchmark evaluates clinical reasoning quality under uncertainty.
The focus is not diagnosis.
The focus is whether a model can reason about stability, trajectory, recovery, boundary pressure, and intervention choice.
Included Datasets
| Dataset | Capability Domain |
|---|---|
| clinical-compensation-vs-recovery-v0.2 | State Reasoning |
| clinical-silent-failure-v0.2 | Trajectory Reasoning |
| clinical-escalation-discipline-v0.2 | Decision Reasoning |
| clinical-recovery-geometry-v0.2 | Recovery Reasoning |
| clinical-constraint-pressure-v0.2 | Boundary Reasoning |
| clinical-boundary-distance-v0.2 | Margin Reasoning |
| clinical-recovery-energy-v0.2 | Recovery Reasoning |
| clinical-trajectory-awareness-v0.2 | Trajectory Reasoning |
| clinical-competing-interventions-v0.2 | Intervention Reasoning |
What v0.2 Adds
v0.2 is harder than v0.1.
It adds:
- counterfactual twin cases
- misleading improvement cases
- same-score different-label cases
- visible severity versus trajectory conflicts
- reserve capacity signals
- support dependency signals
- intervention comparison tasks
The benchmark is designed to reduce shortcut learning.
Capability Areas
The benchmark evaluates:
- hidden support detection
- silent deterioration detection
- escalation discipline
- recovery geometry
- constraint pressure
- margin awareness
- recovery burden estimation
- trajectory awareness
- competing intervention selection
Intended Use
Use this benchmark to evaluate whether a model can reason about clinical stability rather than simply classify visible severity.
A model should not succeed by relying on one variable.
Good performance requires integrating:
- current state
- trend direction
- treatment response
- support dependence
- reserve capacity
- boundary proximity
- recovery burden
- competing intervention effects
Evaluation
Each dataset contains its own scorer.
Binary tasks report:
- accuracy
- precision
- recall
- f1
- confusion matrix
Three-class tasks report:
- accuracy
- macro precision
- macro recall
- macro f1
- confusion matrix
For full-suite reporting, group results by capability domain.
Structural Note
This benchmark evaluates reasoning fidelity through clinical scenarios.
The datasets expose observable variables and labels only.
They do not expose generator logic, hidden scoring rules, or latent construction methods.
License
MIT
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