---
language:
- en
license: mit
pretty_name: Clinical Microvascular Instability
task_categories:
- tabular-classification
tags:
- clarusc64
- clarusc64-benchmark
- stability-reasoning
- clinical
- microcirculation
- capillary-flow
- oxygen-extraction
- trajectory-analysis
- tabular
size_categories:
- n<1K
---

# clinical-microvascular-instability-v0.1

## What this dataset does

This dataset evaluates whether models can detect instability in microvascular circulation.

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

The task is to determine whether microvascular flow remains stable or is moving toward microvascular instability.

## Core stability idea

Microcirculatory stability depends on interaction between capillary flow distribution and tissue oxygen extraction.

Signals that interact include:

- capillary flow proxy trajectory
- oxygen extraction proxy trajectory
- microvascular density proxy
- lactate trajectory
- tissue metabolic demand
- intervention delay

Instability emerges when capillary flow heterogeneity rises while tissue extraction and lactate increase.

## Prediction target

label = 1 → microvascular instability  
label = 0 → stable microcirculation

## Row structure

Each row includes:

- capillary flow proxy trajectory
- oxygen extraction proxy trajectory
- microvascular density proxy trajectory
- lactate trajectory
- tissue demand proxy
- intervention delay

Decoy variables:

- lab_noise
- chart_noise

## Evaluation

Predictions must follow:

scenario_id,prediction

Example:

MV101,0  
MV102,1  

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