Tool
Analysis code and response-level dataset for: An Interpretable Machine Learning Framework for Automated Screening of Patient-Facing LLM Consultations (Medical Aesthetic Consultation)
This deposit contains the complete analysis pipeline (Python) and the response-level dataset (600 LLM-generated responses with blinded expert ratings, readability metrics, and experimental design variables) supporting the manuscript "An Interpretable Machine Learning Framework for Automated Screening of Patient-Facing LLM Consultations: A Comparative Evaluation in Medical Aesthetic Consultation" (Scientific Reports, Submission ID 170dac52-b32e-4486-aca1-739ce0814650). The dataset comprises 600 responses in a fully crossed design (25 seed questions x 6 prompt strategies x 2 query modes x 2 LLMs: DeepSeek-V3 and Kimi). The code reproduces all t
Shucheng, Zhang, Xiaoyue, Sun, Shunyi, Lei, Jiale, Geng, Zixian, Shao, Xiaoqing, Si
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