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.
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Incorporating Missing Data Considerations into Sample Size Calculations for Developing Clinical Prediction Models
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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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