---
language: en
license: mit
task_categories:
  - text-classification
tags:
  - clinical-trials
  - quad-coupling
  - clarus
  - control-geometry
  - counterfactual
  - adversarial
  - sepsis-transition
size_categories:
  - 1K<n<10K
pretty_name: Clinical Quad Infection Buffer Lag Coupling Sepsis Transition v1.1
---

# Clinical Quad Infection Buffer Lag Coupling Sepsis Transition v1.1

## What this repo does

This dataset evaluates whether a model can select the correct control policy when:

- multiple sepsis interventions appear viable
- early signals suggest improvement
- alternative policies produce better long-term outcomes

The task is not prediction.

The task is selecting the correct action under uncertainty, feedback, and misleading signal structure.

## Core quad

The system is defined by four interacting variables.

- infection_load
- buffer_capacity
- lag_burden
- coupling_stress

These variables define system state.

All signals describe how this state evolves under pressure and intervention.

## Clinical variable mapping

| Quad Variable | Clinical Measurements | Typical Indicators |
|---|---|---|
| infection_load | infectious burden, source severity, microbial pressure | source persistence, bacteremia load, uncontrolled infection |
| buffer_capacity | physiological reserve, perfusion reserve, immune reserve | remaining compensation, reserve to tolerate septic stress |
| lag_burden | delayed correction load, unresolved instability debt | late antibiotics, delayed source control, untreated progression |
| coupling_stress | cross-system destabilization linking infection, perfusion, and organs | vasoplegia, inflammatory spillover, organ strain |

## Prediction target

`label_sepsis_transition`

Binary classification.

1 = optimal robust control policy selected  
0 = suboptimal or deceptive policy selected

## Label logic

```text
label = 1 if:
    stabilization_success = 1
    AND trajectory_shift < -0.10
    AND intervention_alignment_score >= 0.60
    AND control_sequence_alignment_score >= 0.60
    AND recovery_consistency_score >= 0.60
    AND policy_regret <= 0.10
    AND policy_robustness >= 0.60
    AND counterfactual_failure_risk <= 0.30
    AND deceptive_signal_score <= 0.50
What v1.1 adds

Earlier versions answer:

where the system is

where it is moving

which intervention may help

whether a control sequence stabilizes it

v1.1 adds:

is this the best available policy

This introduces:

counterfactual comparison

robustness under perturbation

resistance to deceptive signals

Example scenario (realistic numeric row)
scenario_id: seps002

infection_load: 0.91
buffer_capacity: 0.21
lag_burden: 0.76
coupling_stress: 0.80

drift_gradient: 0.65
drift_velocity: 0.82
trajectory_shift: -0.03

intervention_alignment_score: 0.73
control_sequence_alignment_score: 0.65
recovery_consistency_score: 0.53

optimal_policy_score: 0.92
selected_policy_score: 0.58

policy_regret: 0.34
policy_robustness: 0.45
policy_stability_delta: 0.25

local_improvement_score: 0.84
delayed_failure_risk: 0.79
deceptive_signal_score: 0.71
signal_conflict_score: 0.66

short_term_gain_long_term_loss_flag: 1

stabilization_success: 0
label_sepsis_transition: 0

Interpretation:

the intervention produces early improvement

alignment scores appear acceptable

long-term septic control is weak

a superior counterfactual policy exists

deceptive signals mask later collapse

The correct decision is to reject this policy.

Row structure

Each row includes:

system state

trajectory and boundary signals

intervention candidates

control sequence behavior

counterfactual comparisons

policy diagnostics

adversarial signals

outcome fields

Dataset construction

Scenarios are generated by:

sampling septic system states across the quad

generating multiple intervention pathways

simulating outcome trajectories

For each scenario:

optimal_policy_score is assigned from the best trajectory

selected_policy_score is assigned from a candidate path

Derived signals:

policy_regret = optimal − selected

policy_robustness = stability under perturbation

policy_stability_delta = terminal outcome difference

Adversarial structure is introduced by:

high short-term improvement

delayed instability

conflicting subsystem signals

misleading alignment patterns

Counterfactual fields are computed during construction.

Files

data/train.csv
Full dataset with labels

data/tester.csv
Same schema without:

stabilization_success

label_sepsis_transition

scorer.py
Evaluation script

benchmark_spec.json
Formal benchmark definition

dataset_schema.json
Full schema with types and ranges

README.md
This file

Evaluation

Primary metric:

recall_optimal_policy_selection

Secondary metric:

false_robust_policy_rate

Additional diagnostics:

policy_regret_error

policy_robustness_error

policy_stability_delta_error

counterfactual_miss_rate

deceptive_policy_selection_rate

control_sequence_alignment_accuracy

Running the scorer
python scorer.py data/train.csv predictions.csv
Dataset limitations

deceptive_signal_score reflects structural scenario design, not adversarial model input

counterfactual policies are constructed, not exhaustively searched

robustness is simulated

clinical abstraction may omit domain-specific nuance

Intended use

Use for:

benchmarking control decision systems

evaluating policy selection

stress testing models under uncertainty

Not for:

direct clinical decision making

patient-level diagnosis

deployment without validation

standalone decision systems

Structural note

Each version adds one capability:

v0.x: detection and trajectory

v0.6–v0.9: intervention and competition

v1.0: control

v1.1: counterfactual and adversarial evaluation

v1.1 introduces:

multiple valid actions

only one optimal

failure modes that look correct

Production deployment

Applicable to:

clinical systems

infrastructure control

autonomous systems

financial risk

Enterprise and research collaboration

Clarus evaluates system stability.

The focus is:

not what happens next

but whether the chosen action stabilizes the system

License

MIT