This work explores a state-of-the-art foundation vision model, DINOv3 ViT-B/16, to the domain of musculoskeletal (MSK) radiographs using self-supervised learning. Unlike prior work focused on supervised pipelines, we investigate whether and how domain-specific self-supervised adaptation reshapes feature representations to better support downstream anatomical region classification for registry construction. Leveraging a unified dataset of 88,303 plain radiographs across nine anatomical regions, we compare four modeling strategies, including supervised and frozen approaches, as well as self-supervised fine-tuning. Our results demonstrate that off-the-shelf DINOv3 features with a simple linear probe achieve 97.0% balanced accuracy, matching supervised ResNet-18 performance while offering improved macro-precision and F1. Self-supervised domain adaptation alters the embedding geometry, boosting inter-class separability, yet slightly reducing classification accuracy with a linear probe. These findings highlight two takeaways for MSK registries: (1) frozen DINOv3 features provide a robust, label-efficient baseline for large-scale organization, and (2) domain adaptation should be guided by downstream task requirements, particularly when linear classifiers are intended, enabling scalable, label-efficient MSK registries for clinical and research deployment.