Pretrained LLM embeddings show measurable alignment with expert-defined mental-health symptom structure. Fine-tuning enhances this alignment, especially at fine category levels, with larger model sizes improving both zero-shot performance and supervised gains. Residual alignment persists after controlling for linguistic and stylistic confounds, indicating expert structure recovery is level-dependent and requires explicit confound testing.