High-accuracy prediction of mental health scores from English BERT embeddings trained on LLM-generated synthetic self-reports: a synthetic-only method development study

LLM-generated self-reports encode a score-aligned signal that standard ML models can learn, indicating utility for <b>privacy-preserving, synthetic-only</b> prototyping. This is not a clinical tool: results <b>do not</b> imply generalization to real patient text. We clarify terminology (<b>synthetic text</b> vs. <b>real text</b>) and provide a roadmap for external validation, bias/fidelity assessment, and scope-limited deployment considerations before any clinical use.