---
language: en
license: mit
task_categories:
  - text-classification
tags:
  - clinical-trials
  - trajectory-aware
  - clarus
  - stop-point
  - intervention
  - precision
size_categories:
  - n<1K
pretty_name: Clinical Stop Point Precision v0.2
---

# Clinical Stop Point Precision v0.2

## What this is

A small dataset that tests one question:

Can you detect when a clinical system is moving toward stop-point failure, not just under pressure?

This repo focuses on intervention stopping precision.

It models a system where:

- intervention intensity remains high
- physiologic instability may still be active
- recovery signals may be emerging
- overshoot risk rises if stopping decisions lose precision

## Run this first

Generate baseline predictions:

```bash
python baseline_heuristic.py data/tester.csv predictions.csv

Score them:

python scorer.py data/tester.csv predictions.csv

That is enough to see the full evaluation loop.

You will get:

standard metrics

trajectory detection performance

stop-point failure detection errors

What to try next

Replace the baseline.

Build your own model.

Output a file like:

id,prediction_score
0,0.12
1,0.81
2,0.67

Then run:

python scorer.py data/tester.csv your_predictions.csv
What matters

Not just accuracy.

The key signals are:

recall_trajectory_deterioration_detection

false_stable_trajectory_rate

These tell you:

are you catching systems that are getting worse

are you missing hidden stop-point risk

Data

Each row represents a clinical control state.

Core variables:

intervention_intensity

physiologic_instability_index

recovery_signal_strength

overshoot_risk_score

drift_gradient

stop_point_margin

intervention_taper_alignment

coordination_stability_score

Target:

label_stop_point_failure

Important distinction

There are two different components in this repo.

scorer.py

evaluates predictions

domain-agnostic

works across all v0.2 datasets

does not generate predictions

baseline_heuristic.py

generates predictions

domain-specific

uses the variables in this dataset

Do not reuse the heuristic across datasets.

It is only a local reference.

What changed from v0.1

v0.1:

static stop-point classification

v0.2:

adds direction via drift_gradient

This allows you to separate:

pressured but stabilizing intervention states

pressured and deteriorating intervention states

Why this exists

Most models answer:

what is happening now

This tests:

where the system is going

That difference is where stop-point errors appear early.

Files

data/train.csv — training data

data/tester.csv — evaluation data

scorer.py — canonical evaluation script

baseline_heuristic.py — dataset-specific reference model

README.md — dataset card

Evaluation

Primary metric:

recall_trajectory_deterioration_detection

Secondary metric:

false_stable_trajectory_rate

Standard metrics are also reported:

accuracy

precision

recall

f1

The scorer supports binary predictions or score-based predictions.

License

MIT

Structural Note

Clarus datasets are structural instruments.

They are designed to expose instability geometry, not just predict isolated outcomes.

This v0.2 repo adds directional state movement so the dataset can separate static intervention pressure from active deterioration in stop-point control precision.

Production Deployment

This dataset can be used in:

intervention taper studies

stopping-threshold simulation

escalation and de-escalation monitoring

critical care control reasoning

model benchmarking for trajectory-aware clinical control logic

It is suitable for research and prototyping.

It is not a substitute for live clinical judgment.

Enterprise & Research Collaboration

Clarus builds datasets for:

instability detection

trajectory tracking

intervention reasoning

These structures are not domain-bound.

They apply wherever systems move toward or away from failure.

Applicable domains include:

healthcare systems

financial markets

energy infrastructure

logistics networks

artificial intelligence systems

manufacturing systems

supply chains

climate systems

Any environment where:

capacity and demand interact

delays and coupling exist

trajectory determines outcome

This dataset is one instance of a general stability framework.