Datasets:

ClarusC64
/

clinical-renal-filtration-instability-v0.1

scenario_id string	creatinine_t0 float64	creatinine_t1 float64	creatinine_t2 float64	egfr_proxy_t0 float64	egfr_proxy_t1 float64	egfr_proxy_t2 float64	urine_output_t0 float64	urine_output_t1 float64	urine_output_t2 float64	bun_proxy_t0 int64	bun_proxy_t1 int64	bun_proxy_t2 int64	metabolic_waste_proxy float64	intervention_delay int64	lab_noise float64	chart_noise float64	label int64
RF001	0.9	0.9	1	0.82	0.81	0.8	1.6	1.7	1.6	12	13	14	0.4	1	0.31	0.4	0
RF002	1	1.4	2.2	0.78	0.62	0.48	1.5	1.1	0.7	14	20	32	0.76	4	0.33	0.42	1
RF003	0.8	0.8	0.9	0.84	0.83	0.82	1.7	1.8	1.7	11	12	13	0.38	1	0.28	0.36	0
RF004	1.1	1.6	2.5	0.76	0.58	0.44	1.4	1	0.6	15	22	36	0.8	4	0.35	0.43	1
RF005	0.9	0.9	1	0.82	0.81	0.8	1.6	1.7	1.6	12	13	14	0.4	1	0.3	0.38	0
RF006	1.2	1.8	2.9	0.74	0.54	0.4	1.3	0.9	0.5	16	24	40	0.84	4	0.37	0.44	1
RF007	0.8	0.8	0.9	0.84	0.83	0.82	1.7	1.8	1.7	11	12	13	0.38	1	0.27	0.35	0
RF008	1	1.5	2.3	0.78	0.6	0.46	1.5	1.1	0.7	14	21	34	0.78	3	0.34	0.41	1
RF009	0.9	0.9	1	0.82	0.81	0.8	1.6	1.7	1.6	12	13	14	0.4	1	0.29	0.37	0
RF010	1.3	2	3.2	0.72	0.52	0.38	1.2	0.8	0.4	18	28	44	0.88	4	0.36	0.42	1
RF011	0.8	0.8	0.9	0.84	0.83	0.82	1.7	1.8	1.7	11	12	13	0.38	1	0.28	0.36	0
RF012	1.4	2.2	3.6	0.7	0.48	0.34	1.1	0.7	0.3	20	32	50	0.9	4	0.37	0.44	1
RF013	0.9	0.9	1	0.82	0.81	0.8	1.6	1.7	1.6	12	13	14	0.4	1	0.3	0.38	0
RF014	1.1	1.6	2.5	0.76	0.58	0.44	1.4	1	0.6	15	22	36	0.8	3	0.34	0.41	1
RF015	0.8	0.8	0.9	0.84	0.83	0.82	1.7	1.8	1.7	11	12	13	0.38	1	0.27	0.35	0

clinical-renal-filtration-instability-v0.1

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.

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

Signals that interact include:

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

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

Each row includes:

Decoy variables:

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

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.

MIT