---
language:
- en
license: mit
pretty_name: Clinical Oxygen Transport Instability
task_categories:
- tabular-classification
tags:
- clarusc64
- clarusc64-benchmark
- stability-reasoning
- clinical
- oxygen-transport
- perfusion
- hemoglobin
- trajectory-analysis
- tabular
size_categories:
- n<1K
---

# clinical-oxygen-transport-instability-v0.1

## What this dataset does

This dataset evaluates whether models can detect instability caused by failure of oxygen transport to tissues.

Each row represents a simplified oxygen delivery scenario observed across three time points.

The task is to determine whether oxygen delivery remains stable or is moving toward oxygen transport instability.

## Core stability idea

Tissue oxygen delivery depends on interactions between:

- hemoglobin concentration
- oxygen saturation
- cardiac output
- metabolic demand
- lactate accumulation

Instability emerges when oxygen transport capacity declines while metabolic demand increases.

## Prediction target

label = 1 → oxygen transport instability  
label = 0 → stable oxygen delivery

## Row structure

Each row includes:

- hemoglobin trajectory
- oxygen saturation trajectory
- cardiac output proxy trajectory
- oxygen demand proxy
- lactate trajectory
- intervention delay

Decoy variables:

- lab_noise
- chart_noise

## Evaluation

Predictions must follow:

scenario_id,prediction

Example:

OT101,0  
OT102,1  

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