ClarusC64/clinical-quad-evidence-drift-endpoint-signal-claim-language-certainty-narrative-break-v0.1
What this repo does This dataset models narrative continuity break in clinical trial summaries. It predicts when the interaction between evidence consistency, endpoint signal strength, claim strength, and certainty language indicates that the written narrative has drifted away from the underlying trial results. Core quad evidence_consistency_index endpoint_signal_strength_index claim_strength_index certainty_language_index Prediction target label_narrative_break Row structure Each row… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-evidence-drift-endpoint-signal-claim-language-certainty-narrative-break-v0.1.
What this repo does
This dataset models narrative continuity break in clinical trial summaries. It predicts when the interaction between evidence consistency, endpoint signal strength, claim strength, and certainty language indicates that the written narrative has drifted away from the underlying trial results.
Core quad
evidenceconsistencyindex endpointsignalstrengthindex claimstrengthindex certaintylanguage_index
Prediction target
labelnarrativebreak
Row structure
Each row represents a results-to-summary snapshot for a single trial reporting unit. The model predicts whether the coupled evidence and language signals produce a narrative break event within the 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.