ClarusC64/clinical-quad-pvalue-margin-secondary-endpoints-language-publication-pressure-spin-event-v0.1

What this repo does

This dataset models statistical spin formation in clinical trial narratives. It predicts when the interaction between weak p-value margin, many secondary endpoints, high language intensity, and publication pressure indicates a high probability of a spin event where the narrative frames a weak result as strong.

Core quad

pvaluemarginindex secondaryendpointcount languageintensityindex publicationpressureindex

Prediction target

labelspinevent

Row structure

Each row represents a results-to-narrative snapshot for a single reporting unit. The model predicts whether coupled statistical weakness and narrative pressure produce a spin event within the written summary.

Files

data/train.csv data/tester.csv scorer.py

Evaluation

Run predictions on tester.csv Add column prediction Score with scorer.py

License

MIT

Structural Note

This dataset identifies a measurable coupling pattern associated with systemic instability. The sample demonstrates the geometry. Production-scale data determines operational exposure.

What Production Deployment Enables

• 50K–1M row datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support

Small samples reveal structure. Scale reveals consequence.

Enterprise & Research Collaboration

Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.

For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.