metadata
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:
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.