Update README.md

README.md

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 ---
+language:
+- en
 license: mit
+task_categories:
+- text-classification
+tags:
+- clinical
+- clinical-reasoning
+- intervention-selection
+- competing-interventions
+- treatment-choice
+- stabilization-pathway
+- counterfactual-twins
+- stability
+- evidence-fidelity
+size_categories:
+- n<1K
+pretty_name: Clinical Competing Interventions v0.2
 ---
+
+# What this dataset does
+
+This dataset tests whether a model can choose the best stabilising intervention from competing plausible actions.
+
+The task is not diagnosis.
+
+The task is intervention selection under clinical uncertainty.
+
+Each row presents a patient state and three possible intervention options.
+
+The model must predict which intervention is most likely to improve the stabilization trajectory.
+
+# What changed in v0.2
+
+v0.2 adds counterfactual and adversarial cases.
+
+Some rows have similar patient states but different best interventions because response profile, renal risk, ventilation risk, or support needs differ.
+
+Some interventions that look plausible are harmful or insufficient in the given context.
+
+This makes the task harder than simple escalation or severity classification.
+
+# Core stability idea
+
+The best intervention is not always the most aggressive intervention.
+
+A patient may need fluids if fluid responsiveness is good and ventilation risk is low.
+
+A patient may need vasopressors if fluid response is poor and circulatory support is required.
+
+A patient may need oxygen escalation if respiratory load is the primary instability.
+
+A patient may need dialysis if renal failure is the limiting recovery pathway.
+
+A patient may need observation if the system is stable and intervention would add unnecessary burden.
+
+Correct classification requires comparing competing stabilization pathways, not selecting treatment by current severity alone.
+
+# Prediction target
+
+The label column has three classes.
+
+Label 0 means intervention_a is best.
+
+Label 1 means intervention_b is best.
+
+Label 2 means intervention_c is best.
+
+# Row structure
+
+Each row contains:
+
+- scenario_id
+- current_severity
+- map
+- lactate
+- urine_output
+- oxygen_requirement
+- vasopressor_requirement
+- fluid_responsiveness
+- renal_risk
+- ventilation_risk
+- intervention_a
+- intervention_b
+- intervention_c
+- label
+
+current_severity uses:
+
+- low
+- moderate
+- high
+
+oxygen_requirement uses:
+
+- 0 = room air or minimal support
+- 1 = low oxygen requirement
+- 2 = high oxygen requirement
+- 3 = near respiratory boundary
+
+vasopressor_requirement uses:
+
+- none
+- low
+- moderate
+- high
+
+fluid_responsiveness uses:
+
+- good
+- poor
+
+renal_risk uses:
+
+- low
+- medium
+- high
+
+ventilation_risk uses:
+
+- low
+- medium
+- high
+
+intervention fields may include:
+
+- fluids
+- vasopressors
+- oxygen_escalation
+- dialysis
+- observe
+
+# Evaluation
+
+Submissions must contain:
+
+```csv
+scenario_id,prediction
+test_001,0
+test_002,1
+test_003,2
+
+Run:
+
+python scorer.py predictions.csv
+
+Optional truth path:
+
+python scorer.py predictions.csv data/test.csv
+
+The scorer reports:
+
+Accuracy
+Macro precision
+Macro recall
+Macro F1
+Confusion matrix
+Structural Note
+
+This benchmark contains counterfactual and adversarial cases designed to prevent shortcut learning from current severity or a single intervention preference.
+
+The dataset does not expose the hidden rationale behind each label.
+
+The goal is to evaluate whether models can compare competing stabilization pathways under uncertainty.
+
+License
+
+MIT