ClarusC64/clinical-quad-recall-dispersion-lag-enrollment-stall-v0.2

Clinical Quad Recall Dispersion Lag Enrollment Stall v0.2

What this is

A small dataset that tests one question:

Can you detect when enrollment is moving toward stall, not just under pressure?

This repo focuses on trial operations.

It models a system where:

• batch recall disrupts flow
• site dispersion weakens coordination
• replacement lag delays recovery
• active patient count falls under pressure

Run this first

Generate baseline predictions:

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

enrollment stall 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 stall risk

Data

Each row represents a trial system state.

Core variables:

batch_recall_rate

site_dispersion_index

replacement_lag_days

active_patients

coherence_risk_score

enrollment_pressure_index

coordination_stability_score

drift_gradient

Target:

label_enrollment_stall

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 stall classification

v0.2:

adds direction via drift_gradient

This allows you to separate:

pressured but recovering enrollment systems

pressured and deteriorating enrollment systems

Why this exists

Most models answer:

what is happening now

This tests:

where the system is going

That difference is where stall risk appears 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 enrollment pressure from active deterioration in trial recovery capacity.

Production Deployment

This dataset can be used in:

clinical trial operations research

site recovery monitoring

enrollment risk studies

sponsor portfolio simulations

model benchmarking for trajectory-aware trial reasoning

It is suitable for research and prototyping.

It is not a substitute for operational 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.