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infection_pressure_index float64 | inflammatory_buffer_capacity float64 | latent_coupling_pressure float64 | compensation_fatigue_score float64 | drift_gradient float64 | coherence_stability_score float64 | context_integrity_score float64 | decision_readiness_score float64 | label_infection_inflammatory_escalation int64 |
|---|---|---|---|---|---|---|---|---|
0.89 | 0.85 | 0.16 | 0.21 | -0.18 | 0.87 | 0.84 | 0.9 | 0 |
0.81 | 0.77 | 0.27 | 0.33 | -0.1 | 0.75 | 0.72 | 0.78 | 0 |
0.68 | 0.63 | 0.44 | 0.48 | 0.07 | 0.59 | 0.56 | 0.62 | 1 |
0.59 | 0.55 | 0.56 | 0.6 | 0.16 | 0.48 | 0.45 | 0.5 | 1 |
0.51 | 0.47 | 0.67 | 0.72 | 0.25 | 0.39 | 0.37 | 0.41 | 1 |
0.85 | 0.81 | 0.21 | 0.27 | -0.15 | 0.83 | 0.8 | 0.86 | 0 |
0.45 | 0.41 | 0.75 | 0.81 | 0.32 | 0.33 | 0.31 | 0.35 | 1 |
0.74 | 0.69 | 0.36 | 0.41 | 0.02 | 0.66 | 0.63 | 0.7 | 0 |
0.56 | 0.52 | 0.61 | 0.66 | 0.19 | 0.44 | 0.41 | 0.46 | 1 |
0.91 | 0.87 | 0.14 | 0.2 | -0.2 | 0.89 | 0.86 | 0.92 | 0 |
Clinical Latent Cross Coupling Infection Inflammatory Escalation v0.2
What this is
A small dataset that tests one question:
Can you detect when an infection-inflammatory system is moving toward hidden escalation, not just carrying visible strain?
This repo focuses on latent cross coupling between infection pressure and inflammatory buffering.
It models a system where:
- infection pressure may rise
- inflammatory buffer capacity may erode
- latent coupling pressure may intensify
- compensation fatigue may accumulate before overt escalation 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
infection-inflammatory escalation 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 infection-inflammatory escalation
Data
Each row represents a latent infection-inflammatory coupling state.
Core variables:
infection_pressure_index
inflammatory_buffer_capacity
latent_coupling_pressure
compensation_fatigue_score
drift_gradient
coherence_stability_score
context_integrity_score
decision_readiness_score
Target:
label_infection_inflammatory_escalation
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 latent coupling classification
v0.2:
adds direction via drift_gradient
This allows you to separate:
strained but stabilizing coupling states
strained and deteriorating coupling states
Why this exists
Most models answer:
what is happening now
This tests:
where the hidden interaction is going
That difference is where failure 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 infection-inflammatory strain from active deterioration in latent cross coupling.
Production Deployment
This dataset can be used in:
inflammatory escalation research
sepsis transition monitoring
hidden coupling benchmarking
critical care trajectory modeling
model benchmarking for trajectory-aware latent coupling 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.
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