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id
int64
1
10
recruitment
stringclasses
3 values
site_mix
stringclasses
2 values
endpoint
stringclasses
3 values
protocol_change
stringclasses
3 values
signal
stringclasses
10 values
label
int64
0
2
1
on_target
balanced
primary_met
none
all nodes aligned
0
2
slipping
balanced
primary_met
minor
early strain but recoverable
1
3
slipping
skewed
primary_missed
minor
site mix drift undermines endpoint
1
4
off_target
skewed
primary_missed
major
protocol shifts chase recruitment failure
2
5
on_target
skewed
secondary_only
minor
endpoint tension from site bias
1
6
off_target
balanced
secondary_only
major
endpoint rewrite to mask enrollment collapse
2
7
slipping
skewed
secondary_only
major
quad misalignment locks in collapse
2
8
on_target
balanced
primary_met
minor
small change with intact coherence
0
9
on_target
skewed
primary_met
none
site skew but endpoint still robust
1
10
off_target
skewed
primary_missed
major
recruitment failure drives unstable decisions
2

Clinical Quad Recruitment Endpoint Coherence Collapse v0.2

What this dataset does

It tests whether a model can detect when recruitment pressure drives endpoint instability.

The quad nodes

  • recruitment
  • site_mix
  • endpoint
  • protocol_change

Labels

0 coherent

  • recruitment on target
  • sites balanced
  • primary endpoint met
  • no major protocol change

1 tradeoff

  • some drift or tension
  • endpoint credibility pressured but not fully broken

2 collapse

  • recruitment failure drives major protocol changes
  • endpoint integrity collapses under site drift and missed primary outcomes

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
  • Predictions can be label, prediction, or output text

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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