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trial_id
stringclasses
10 values
exposure_level
stringclasses
3 values
metabolic_clearance
stringclasses
3 values
co_medication_load
stringclasses
3 values
toxicity_signal
stringclasses
3 values
label
stringclasses
3 values
signal
stringclasses
10 values
T201
high
low
high
escalating
collapse_risk
High exposure with low clearance plus high co-med load and escalating toxicity.
T202
moderate
normal
low
stable
coherent
Moderate exposure with normal clearance and low co-med load with stable toxicity.
T203
high
normal
high
rising
tradeoff
High exposure and high co-med load with rising toxicity but clearance normal.
T204
low
high
moderate
stable
coherent
Low exposure with high clearance and moderate co-med load with stable toxicity.
T205
moderate
low
high
escalating
collapse_risk
Moderate exposure with low clearance and high co-med load with escalating toxicity.
T206
high
high
low
stable
tradeoff
High exposure with high clearance and low co-med load with stable toxicity.
T207
moderate
normal
moderate
rising
tradeoff
Moderate signals with rising toxicity creates strain.
T208
low
normal
high
rising
tradeoff
Low exposure but high co-med load and rising toxicity.
T209
high
low
moderate
escalating
collapse_risk
High exposure with low clearance and escalating toxicity under moderate co-med load.
T210
moderate
high
low
stable
coherent
Moderate exposure with high clearance and low co-med load with stable toxicity.

Clinical Quad Exposure Metabolism CoMed Toxicity v0.2

What this dataset does

It tests whether a model can detect when toxicity escalation is driven by four coupled pharmacology signals.

Quad nodes

  • exposure_level
  • metabolic_clearance
  • co_medication_load
  • toxicity_signal

Labels

coherent

  • stable toxicity
  • low or moderate exposure
  • normal or high clearance
  • low or moderate co-med load

tradeoff

  • mixed strain
  • toxicity rising or capacity mismatch without full collapse pattern

collapse_risk

  • toxicity escalating
  • low clearance
  • high co-med load
  • moderate or high exposure

What changed in v0.2

  • Version bumped so scorer updates are visible
  • New scorer with validation, confusion, and error sampling
  • Added rule_pred and risk_score diagnostics

Files

data/train.csv
data/test.csv
scorer.py

Run scoring

python scorer.py --preds_csv predictions.csv --gold_csv data/test.csv

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