ClarusC64/clinical-therapeutic-pathway-adaptive-navigation-v0.1
What this dataset tests Whether a model can choose the best next pathway movebased on manifold position and invariant branch signatures. Navigation actions stay_course adjust_dose switch_class pause_observe add_support_module deescalate_exit_loop urgent_escalation Outputs one recommended action 2 to 3 microsteps one navigation label Labels coherent_navigation partially_coherent incoherent_navigation Typical failures protocol reflex that ignores invariant… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-therapeutic-pathway-adaptive-navigation-v0.1.
What this dataset tests
Whether a model can choose the best next pathway move based on manifold position and invariant branch signatures.
Navigation actions
- stay_course
- adjust_dose
- switch_class
- pause_observe
- addsupportmodule
- deescalateexitloop
- urgent_escalation
Outputs
- one recommended action
- 2 to 3 microsteps
- one navigation label
Labels
- coherent_navigation
- partially_coherent
- incoherent_navigation
Typical failures
- protocol reflex that ignores invariant signatures
- repeating tolerance loops
- ignoring contraindications
- switching classes when the real issue is exposure gap
Suggested prompt wrapper
System
You navigate a therapeutic pathway.
User
Current Position {currentpositionsummary}
Invariant Signature {invariant_signature}
Predicted Basin {predictednextbasin}
Risk Flags {risk_flags}
Constraints {constraintsandcontraindications}
Return
- one recommended action
- 2 microsteps
- one label
Citation
ClarusC64 dataset family