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trial_id
stringclasses
10 values
protocol_deviation
stringclasses
3 values
staffing_drift
stringclasses
2 values
adjudication_variance
stringclasses
2 values
missingness_bias
stringclasses
2 values
label
stringclasses
3 values
signal
stringclasses
10 values
T501
high
yes
high
high
collapse_risk
All nodes high with staffing drift. Process control collapses.
T502
low
no
low
low
coherent
All nodes low with stable staffing. Clean operations.
T503
high
no
high
low
tradeoff
High deviation and adjudication variance but missingness contained and staffing stable.
T504
medium
yes
high
high
tradeoff
Staffing drift plus high adjudication and missingness creates strain without full max collapse.
T505
high
yes
low
high
tradeoff
Deviation and missingness high with staffing drift but adjudication stable.
T506
high
yes
high
low
tradeoff
Deviation high with staffing drift and adjudication variance but missingness contained.
T507
low
yes
low
high
tradeoff
Staffing drift plus missingness bias creates governance strain.
T508
medium
no
high
high
tradeoff
High adjudication and missingness with no staffing drift.
T509
low
no
high
low
tradeoff
Adjudication variance high while other nodes stable.
T510
medium
no
low
low
coherent
Mostly stable with one medium deviation and no staffing drift.

Clinical Quad Protocol Deviation Staffing Drift Adjudication Variance Missingness Bias v0.2

What this dataset does

It tests whether a model can detect operational collapse risk in trial conduct.

The quad nodes

  • protocol_deviation
  • staffing_drift
  • adjudication_variance
  • missingness_bias

Labels

coherent

  • stable staffing
  • low drift and low bias
  • operations remain controlled

tradeoff

  • mixed strain
  • issues exist but do not meet collapse pattern

collapse_risk

  • all level nodes high and staffing drift present
  • process control fails across the trial pipeline

What changed in v0.2

  • Fixed repo name typo from linical to clinical
  • Version bumped so scorer updates are visible
  • New scorer with validation, confusion, and error sampling
  • Added risk_score and rule_pred 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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