Missing predictor data increases the minimum sample size needed for stable, well-calibrated clinical prediction models, with context-specific inflation factors up to 2x, via adaptations to posterior sampling calculations.
(57) Friedman, J.; Hastie, T.; Tibshirani, R
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A multi-agent LLM system for automated chart summarization was developed, evaluated against physician gold standards, deployed at Stanford Thoracic Tumor Board, and monitored post-deployment, with validation of LLM-as-judge scoring.
Network analysis of software mentions in biomedical papers identifies high-centrality foundational packages in three major open-source ecosystems.
A software framework integrates heterogeneous causal inference, policy learning, mediation, forecasts, variance reduction, and anytime-valid inference into one AI-orchestratable interface for business experimentation.
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Incorporating Missing Data Considerations into Sample Size Calculations for Developing Clinical Prediction Models
Missing predictor data increases the minimum sample size needed for stable, well-calibrated clinical prediction models, with context-specific inflation factors up to 2x, via adaptations to posterior sampling calculations.
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Development, Evaluation, and Deployment of a Multi-Agent System for Thoracic Tumor Board
A multi-agent LLM system for automated chart summarization was developed, evaluated against physician gold standards, deployed at Stanford Thoracic Tumor Board, and monitored post-deployment, with validation of LLM-as-judge scoring.
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Biomedical Open Source Software: Crucial Packages and Hidden Heroes
Network analysis of software mentions in biomedical papers identifies high-centrality foundational packages in three major open-source ecosystems.
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Closing the Loop: A Software Framework for AI to Support Business Decision Making
A software framework integrates heterogeneous causal inference, policy learning, mediation, forecasts, variance reduction, and anytime-valid inference into one AI-orchestratable interface for business experimentation.