In this paper, we address Task 1 on adverse drug event (ADE) detection and Task 8 on MultiClinNER at SMM4H-HeaRD 2026. ADE detection is formulated as a multilingual binary classification problem over social media posts spanning German, French, Russian, English, Mandarin and Japanese, with zero-shot on Farsi. Using XLM-RoBERTa-Large with a dual-pooling head, combined with stratified sampling, language-conditioned inputs, translation-based augmentation, and calibrated ensembling, our model achieves a macro F1 score of 0.6088, surpassing both the competition mean (0.5465) and median (0.5798). Our work in MultiClinNER targets clinical NER for English text. Using GLiNER-large with sliding-window inference, query augmentation, and calibrated thresholds, it achieves strict F1 scores of 0.7591 (Disease), 0.7263 (Procedure), and 0.6733 (Symptom), outperforming a PubMedBERT baseline across all entities.