Datasets:

ClarusC64
/

clinical-sepsis-trajectory-instability-v0.1

scenario_id string	temp_t0 float64	temp_t1 float64	temp_t2 float64	map_t0 int64	map_t1 int64	map_t2 int64	lactate_t0 float64	lactate_t1 float64	lactate_t2 float64	wbc_t0 float64	wbc_t1 float64	wbc_t2 float64	resp_rate_t0 int64	resp_rate_t1 int64	resp_rate_t2 int64	fluid_response float64	antibiotic_delay int64	renal_marker float64	inflammation_noise float64	documentation_noise float64	label int64
S001	38.1	38	37.8	72	73	74	2.4	2.3	2.1	13.2	12.8	12.1	22	21	20	0.72	1	1.1	0.44	0.31	0
S002	37.9	38.2	38.6	74	71	67	2.1	2.5	3.2	12.1	13.6	15.8	21	24	28	0.41	3	1.4	0.46	0.34	1
S003	38.4	38.2	38	70	72	73	3.1	2.9	2.5	15	14.4	13.2	25	23	22	0.68	1	1.3	0.5	0.29	0
S004	37.7	38	38.3	76	72	68	1.9	2.4	3	11.8	13.1	15.1	20	23	27	0.38	4	1.5	0.43	0.36	1
S005	38	37.9	37.7	71	72	74	2.6	2.4	2.2	14.1	13.3	12.7	23	22	20	0.75	1	1	0.47	0.3	0
S006	38.2	38.5	38.7	73	70	66	2.3	2.8	3.5	13	14.7	16.5	22	25	29	0.36	3	1.6	0.51	0.32	1
S007	37.8	37.7	37.6	75	76	76	2	1.9	1.8	12.6	12	11.4	21	20	19	0.8	1	0.9	0.39	0.28	0
S008	38.5	38.6	38.8	72	69	65	2.7	3	3.8	14.3	15.5	17.4	24	27	31	0.34	4	1.8	0.55	0.37	1
S009	38.3	38.1	37.9	69	70	72	3.4	3	2.6	15.6	14.8	13.5	26	24	22	0.66	2	1.4	0.52	0.33	0
S010	37.6	37.9	38.2	76	74	70	1.8	2.1	2.8	10.9	12.2	14.4	20	22	26	0.43	3	1.5	0.42	0.35	1
S011	38.1	38	37.8	73	74	75	2.5	2.3	2.1	13.4	12.9	12.2	22	21	20	0.71	1	1.1	0.45	0.31	0
S012	38.1	38	37.8	73	72	70	2.5	2.7	3.2	13.4	14	15.2	22	24	27	0.39	3	1.4	0.45	0.31	1
S013	37.9	37.8	37.7	74	75	76	2.2	2	1.9	12.7	12.1	11.6	21	20	19	0.77	1	0.9	0.4	0.27	0
S014	38.4	38.3	38.1	70	71	72	3.2	2.8	2.4	15.2	14.2	13	25	23	21	0.64	2	1.2	0.53	0.34	0
S015	38.4	38.3	38.1	70	69	67	3.2	3.3	3.7	15.2	15.6	16.9	25	27	30	0.33	4	1.7	0.53	0.34	1
S016	37.7	37.7	37.6	76	77	78	1.9	1.8	1.7	11.9	11.5	11.2	20	19	19	0.82	1	0.8	0.38	0.3	0
S017	38.6	38.8	39	74	71	66	2.4	2.9	3.6	13.5	15	17.2	23	26	30	0.35	4	1.9	0.57	0.39	1
S018	38.2	38	37.9	71	72	74	2.8	2.5	2.2	14.4	13.5	12.6	24	22	20	0.7	1	1	0.48	0.32	0
S019	37.8	38.1	38.4	75	72	68	2	2.4	3.1	12	13.4	15.6	21	24	28	0.4	3	1.6	0.44	0.35	1
S020	38	37.9	37.8	72	73	74	2.3	2.2	2	13	12.5	12	22	21	20	0.74	1	1	0.41	0.28	0
S021	38.5	38.7	38.9	73	70	66	2.6	3.1	3.9	14	15.8	17.7	24	27	31	0.32	4	1.8	0.56	0.37	1
S022	38.3	38.1	38	70	71	73	3.3	2.9	2.5	15.4	14.6	13.3	26	24	22	0.65	2	1.3	0.51	0.32	0
S023	37.9	38.2	38.5	74	71	67	2.1	2.6	3.3	12.4	14.1	16	21	24	29	0.37	3	1.6	0.47	0.36	1
S024	38.1	37.9	37.8	73	74	75	2.4	2.2	2	13.1	12.6	12	22	21	20	0.76	1	1	0.43	0.3	0
S025	38.7	38.9	39.1	72	69	64	2.8	3.2	4.1	14.6	16	18.2	25	28	32	0.31	4	2	0.58	0.4	1
S026	37.8	37.7	37.6	76	77	77	2	1.9	1.8	12.3	11.9	11.4	21	20	19	0.81	1	0.9	0.39	0.27	0
S027	38	38.2	38.6	75	72	68	2.2	2.7	3.4	12.8	14.3	16.3	22	25	29	0.36	3	1.7	0.49	0.35	1
S028	38.4	38.2	38	70	72	73	3	2.7	2.3	15.1	14	12.9	25	23	21	0.67	1	1.2	0.52	0.31	0
S029	37.7	38	38.3	76	73	69	1.9	2.3	3	11.7	13	15	20	23	27	0.42	3	1.5	0.42	0.34	1
S030	38.2	38	37.8	72	73	75	2.6	2.4	2.1	13.8	13	12.1	23	21	20	0.73	1	1.1	0.46	0.29	0

clinical-sepsis-trajectory-instability-v0.1

What this dataset does

This dataset tests whether a model can classify sepsis trajectory instability from short clinical proxy sequences.

Each row describes a patient-like scenario across three time points.

The task is to predict whether the scenario is moving toward instability or remaining stable.

Core stability idea

Sepsis instability does not depend on one variable alone.

A patient may show an abnormal value and still recover.

Another patient may show only moderate abnormalities but deteriorate when trends combine with weak response and delayed intervention.

The dataset tests interaction reasoning across:

temperature trajectory
blood pressure trajectory
lactate trajectory
inflammatory burden
respiratory strain
fluid response
intervention delay
renal stress

Prediction target

label = 1 means sepsis trajectory instability.

label = 0 means stable or recovering trajectory.

Row structure

Each row includes:

scenario_id
temperature values across three time points
MAP values across three time points
lactate values across three time points
WBC values across three time points
respiratory-rate values across three time points
fluid response
antibiotic delay
renal marker
decoy variables
label

Decoy variables:

inflammation_noise
documentation_noise

These appear meaningful but do not define the target alone.

Evaluation

Predictions must use this format:

scenario_id,prediction
S101,0
S102,1

Run:

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

The scorer returns:

accuracy
precision
recall
f1
confusion matrix
dataset integrity checks
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 ClarusC64 stability-reasoning benchmark family. Datasets share a latent stability geometry but expose only observable proxy variables.

Production Deployment

This dataset is intended as a compact benchmark for evaluating reasoning over unstable trajectories.

It is not a clinical decision tool.

Enterprise & Research Collaboration

This dataset is designed to invite collaboration around latent stability reasoning, cross-domain transfer, and interaction-based evaluation.

License

MIT

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