ClarusC64/clinical-glucose-regulation-instability-v0.1

clinical-glucose-regulation-instability-v0.1

What this dataset does

This dataset evaluates whether models can detect instability in glucose regulation.

Each row represents a simplified glucose control scenario observed across three time points.

The task is to determine whether metabolic glucose regulation remains stable or is moving toward regulatory instability.

Core stability idea

Glucose stability depends on feedback between insulin signaling, hepatic buffering, and metabolic demand.

Signals that interact include:

• —glucose trajectory
• —insulin response proxy trajectory
• —hepatic buffering proxy trajectory
• —ketone trajectory
• —metabolic demand proxy
• —intervention delay

Instability emerges when glucose rises while insulin response and hepatic buffering fail to stabilize metabolic demand.

Prediction target

label = 1 → glucose regulation instability label = 0 → stable metabolic glucose control

Row structure

Each row includes:

• —glucose trajectory
• —insulin response proxy trajectory
• —hepatic buffer proxy trajectory
• —ketone proxy trajectory
• —metabolic demand proxy
• —intervention delay

Decoy variables:

• —lab_noise
• —chart_noise

Evaluation

Predictions must follow:

scenario_id,prediction

Example:

GR101,0 GR102,1

Run:

python scorer.py --predictions predictions.csv --truth data/test.csv --output metrics.json

Metrics produced:

accuracy precision recall f1 confusion matrix dataset integrity diagnostics

Structural Note

This dataset reflects latent stability geometry through observable proxies.

The generator and latent rule structure are not included.

This dataset is part of the Clarus Stability Reasoning Benchmark.

License

MIT