Datasets:
id int64 1 10 | enrollment_stability stringclasses 3 values | protocol_stress stringclasses 3 values | data_integrity stringclasses 3 values | safety_signal stringclasses 3 values | signal stringclasses 10 values | label int64 0 2 |
|---|---|---|---|---|---|---|
1 | stable | low | clean | none | All nodes stable and no safety signal | 0 |
2 | stable | medium | clean | none | Some protocol stress but data stays clean | 0 |
3 | slow | medium | soft | weak | Early strain with soft data and weak safety signal | 1 |
4 | spiky | high | questionable | weak | Operational instability plus data integrity loss | 2 |
5 | stable | high | questionable | strong | High stress and strong safety signal with questionable data | 2 |
6 | slow | low | soft | none | Enrollment slows and data softens without safety signal | 1 |
7 | spiky | medium | questionable | none | Site chaos drives data credibility loss | 2 |
8 | stable | medium | soft | weak | Manageable strain with weak safety signal | 1 |
9 | slow | high | questionable | strong | Multi node failure with strong safety pressure | 2 |
10 | spiky | low | clean | weak | Spiky enrollment but endpoints and data still clean | 1 |
Clinical Quad Trial Collapse Surface v0.2
What this dataset does
It tests whether a model can classify when a trial moves onto a collapse surface.
You get four coupled signals.
- enrollment_stability
- protocol_stress
- data_integrity
- safety_signal
You return a label.
- 0 coherent
- 1 tradeoff
- 2 collapse
How to read the labels
0 coherent
- stable enrollment
- low to medium stress
- clean data
- no safety signal
1 tradeoff
- strain exists
- credibility still holds
- risk is rising
2 collapse
- questionable data with high stress or spiky enrollment
- strong safety signal on a fragile data surface
Files
- data/train.csv
- data/test.csv
- scorer.py
Run scoring
predictions.csv
- id,label
Run
python scorer.py --preds_csv predictions.csv --gold_csv data/test.csv
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