ClarusC64/linical-quad-protocol-deviation-staffing-drift-adjudication-variance-missingness-bias-v0.1
Clinical Quad: Protocol Deviations × Staffing Drift × Adjudication Variance × Missingness Bias This dataset targets a “trial looks clean on paper” failure mode. Sites drift in staffing.Protocol deviations rise.Endpoint adjudication becomes inconsistent.Missing data stops being random. The four-way coupling can create false stability or false efficacy. Variables protocol_deviation (low | medium | high) staffing_drift (yes | no) adjudication_variance (low | high)… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/linical-quad-protocol-deviation-staffing-drift-adjudication-variance-missingness-bias-v0.1.
Clinical Quad: Protocol Deviations × Staffing Drift × Adjudication Variance × Missingness Bias
This dataset targets a “trial looks clean on paper” failure mode.
Sites drift in staffing. Protocol deviations rise. Endpoint adjudication becomes inconsistent. Missing data stops being random.
The four-way coupling can create false stability or false efficacy.
Variables
- protocol_deviation (low | medium | high)
- staffing_drift (yes | no)
- adjudication_variance (low | high)
- missingness_bias (low | high)
Labels
- coherent
- tradeoff
- collapse_risk
Collapse rule
protocoldeviation high staffingdrift yes adjudicationvariance high missingnessbias high
Why it matters
You want models to flag when validity fails through operations, not biology.