---
language:
  - en
license: mit
pretty_name: Clinical Personal Deviation Vector Detection v0.1
dataset_name: clinical-personal-deviation-vector-detection-v0.1
tags:
  - clarusc64
  - clinical
  - n-of-1
  - deviation
  - manifold
  - early-detection
task_categories:
  - text-classification
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.csv
      - split: test
        path: data/test.csv
---

What this dataset tests  

Whether a model can detect deviation from a person's own coherent basin  
using baseline envelope and coupling structure.

Required outputs  

- deviation_vector  
- deviation_severity_score_0_100  
- first_system_departing  

Deviation vector fields  

- direction  
- magnitude  
- velocity  
- coupling_loss  
- onset_time  
- cross_modal_consensus  

First system labels  

- sleep_circadian  
- autonomic  
- immune_inflammatory  
- metabolic  
- neurocognitive  
- gut_microbiome  
- behavior_load  
- subjective_experience  

Typical failures  

- treating population thresholds as baseline  
- outputting severity with no vector structure  
- ignoring coupling changes  

Suggested prompt wrapper  

System  

You detect deviation from a personal baseline.

User  

Baseline signature  
{baseline_signature}

Baseline envelope  
{baseline_envelope}

Recent data  
Sleep: {recent_data_sleep}  
Wearables: {recent_data_wearables}  
Labs: {recent_data_labs}  
Behavior: {recent_data_behavior}  
Subjective: {recent_data_subjective}  

Coupling changes  
{coupling_changes}

Return  

- deviation vector with required fields  
- severity score  
- first system departing  
- one sentence evidence  

Citation  

ClarusC64 dataset family