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id
int64
1
10
population_shift
stringclasses
3 values
protocol_deviation_rate
stringclasses
3 values
site_variance_level
stringclasses
3 values
endpoint_fragility
stringclasses
3 values
signal
stringclasses
10 values
label
int64
0
2
1
none
low
low
low
Population stable with low deviations and uniform sites. Endpoint robust
0
2
minor
medium
low
low
Small population shift with manageable deviations. Endpoint remains robust
1
3
minor
medium
medium
medium
Population shift plus site drift creates early endpoint fragility
1
4
major
high
medium
high
Major population shift with high deviations undermines endpoint validity
2
5
none
high
high
high
High deviations and site variance fragment endpoint despite stable population
2
6
major
low
medium
medium
Population shifts but deviations low. Endpoint pressured by site effects
1
7
major
medium
high
high
All nodes misalign. Endpoint becomes fragile and unstable
2
8
minor
low
high
medium
Population mostly stable but site variance drives fragility
1
9
none
medium
high
high
Site variance dominates and endpoint credibility breaks
2
10
minor
high
low
medium
Deviations rising with mild population shift creates endpoint fragility
2

Clinical Quad Population Shift Protocol Deviation Site Variance Endpoint Fragility v0.2

What this dataset does

It tests whether a model can detect when clinical trial endpoints lose credibility under quad coupling.

Quad nodes

  • population_shift
  • protocol_deviation_rate
  • site_variance_level
  • endpoint_fragility

Labels

0 coherent

  • Stable population
  • Low deviations
  • Low site variance
  • Endpoint robust

1 tradeoff

  • Some drift exists
  • Endpoint still usable
  • Risk is present but not terminal

2 collapse

  • Endpoint credibility breaks
  • Drift becomes structural
  • Site or deviation pressure overwhelms interpretation

What changed in v0.2

  • Version bumped to make scorer changes visible
  • New scorer with validation and id-based matching
  • Confusion and error sampling added
  • Risk score and rule_pred diagnostics added

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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