This paper describes the BIT.UA system for the MultiClinNER shared task at #SMM4H–HeaRD 2026, targeting multilingual clinical named entity recognition across seven languages for three entity types (Disease, Procedure, Symptom). We extend the Multi-Head CRF architecture, originally developed for multi-class NER on Spanish clinical text, to the multilingual setting. To enable joint multi-entity training despite per-entity text variations in the dataset, we develop an adaptive text consolidation pipeline that preserves over 94% of annotations. Our central finding is that a single xlm-roberta-large model, trained jointly on all seven languages and three entity types, achieves competition rank 2 for five of seven languages, outperforming dedicated monolingual models by up to +6.94 F1 points, while requiring only a single set of weights. Ensembling multiple seeds of this model achieves rank 1 for those five languages, and combining it with monolingual models yields rank 1 for the remaining two. Code and models are publicly available at https://github.com/ieeta-pt/Multi-Head-CRF/tree/MultiClinNER and https://huggingface.co/collections/IEETA/multiclinner-models.