README.md

---
language: en
license: mit
task_categories:
  - text-classification
tags:
  - clinical-trials
  - trajectory-aware
  - clarus
  - diagnostic-logic
  - fragility
  - atlas
size_categories:
  - n<1K
pretty_name: Clinical Diagnostic Logic Fragility Atlas v0.2
---

# Clinical Diagnostic Logic Fragility Atlas v0.2

## What this is

A small dataset that tests one question:

Can you detect when diagnostic logic is moving toward fragility, not just carrying ambiguity?

This repo focuses on diagnostic logic breakdown under clinical reasoning pressure.

It models a system where:

- diagnostic signal consistency may weaken
- hypothesis conflict may rise
- evidence may fragment
- inference stability may erode before overt decision failure appears

## 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

diagnostic fragility 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 diagnostic collapse

Data

Each row represents a diagnostic reasoning state.

Core variables:

diagnostic_signal_consistency

hypothesis_conflict_index

evidence_fragmentation_score

inference_stability_score

drift_gradient

logic_gap_pressure

context_loss_index

decision_coherence_score

Target:

label_diagnostic_logic_fragility

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 diagnostic fragility classification

v0.2:

adds direction via drift_gradient

This allows you to separate:

strained but stabilizing reasoning systems

strained and deteriorating reasoning systems

Why this exists

Most models answer:

what is happening now

This tests:

where the reasoning structure is going

That difference is where fragility 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 diagnostic strain from active deterioration in reasoning coherence.

Production Deployment

This dataset can be used in:

diagnostic reasoning research

differential conflict monitoring

clinical synthesis benchmarking

decision support stress testing

model benchmarking for trajectory-aware diagnostic logic reasoning

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.