ClarusC64/clinical-renal-filtration-instability-v0.1

clinical-renal-filtration-instability-v0.1

What this dataset does

This dataset evaluates whether models can detect instability in renal filtration and waste clearance.

Each row represents a simplified kidney function monitoring scenario observed across three time points.

The task is to determine whether renal filtration remains stable or is moving toward renal instability.

Core stability idea

Kidney stability depends on interaction between filtration capacity and metabolic waste production.

Signals that interact include:

• —creatinine trajectory
• —estimated filtration proxy
• —urine output trajectory
• —blood urea nitrogen proxy
• —metabolic waste proxy
• —intervention delay

Instability emerges when filtration capacity declines while metabolic waste accumulation rises.

Prediction target

label = 1 → renal filtration instability label = 0 → stable renal filtration

Row structure

Each row includes:

• —creatinine trajectory
• —eGFR proxy trajectory
• —urine output trajectory
• —BUN proxy trajectory
• —metabolic waste proxy
• —intervention delay

Decoy variables:

• —lab_noise
• —chart_noise

Evaluation

Predictions must follow:

scenario_id,prediction

Example:

RF101,0 RF102,1

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