Validation of a Small Language Model for DSM-5 Substance Category Classification in Child Welfare Records
This study validates that a locally hosted 20-billion-parameter small language model can reliably classify specific DSM-5 substance categories within child welfare investigation narratives, achieving near-perfect agreement with human experts for five major substance types despite limitations with low-prevalence categories.