This paper presents our approach to Task 5 of the #SMM4H-HeaRD 2026 Workshop, which focuses on detecting patient metadata in SARS-CoV-2 sequencing articles as a binary classification task. We explore both encoder-based and large language model (LLM) approaches, using BioM-BERT as a baseline and Mistral-Nemo as the LLM. To improve performance, we propose a paraphrase-based data augmentation pipeline using Qwen3, where paraphrased training and validation instances are added for fine-tuning. For the LLM, we perform prompt refinement and error analysis, while for the encoder-based model, we reformulate the task as a hypothesis-conditioned classification task inspired by Natural Language Inference (NLI). Our methods improve both models: Mistral-Nemo increases from 0.423 to 0.750 F1, and BioM-BERT from 0.801 to 0.821 on the validation set. Although Mistral-Nemo does not surpass BioM-BERT, our best BioM-BERT model achieves an F1-score of 0.786 on the test set, outperforming the mean and median of competing systems. To support reproducibility, we release our best-performing model on Hugging Face.