In this paper, we describe systems for two #SMM4H-HeaRD 2026 shared tasks. Task 6 asks for per-axis TNM cancer staging from free-text TCGA pathology reports under severe label imbalance and long-document constraints. We fine-tune GatorTron-base separately on each axis using Focal loss with class weights and a pooled [CLS]–mean representation, reaching macro F1 of 0.700 (T), 0.774 (N), and 0.640 (M) on test set 2 against a baseline of 0.454, 0.591, and 0.554 respectively. Task 7 asks for span-level detection of opioid-related ClinicalImpacts and SocialImpacts in first-person Reddit posts. We combine DeBERTa-large and PubMedBERT (two seeds each) in a uniform-weight ensemble with boundary-aware loss, entity-replacement augmentation, and a first-person post filter, achieving strict F1 of 0.51 and relaxed F1 of 0.60, above both the task mean (0.46 / 0.55) and median (0.48 / 0.58).

Depression detection from speech aims to findsigns of depression using behavioral signals.This approach enables early mental healthscreening and makes it scalable. However, thetask is tough because of subtle acoustic cues,differences among speakers, and language-specific patterns. In this work, we introduceour system for the Shared Task on DepressionDetection in Dravidian Languages (DD-DL)at DravidianLangTech@ACL 2026. We fo-cus on speech in Tamil and Malayalam. Weexplore pretrained self-supervised speech en-coders, including HuBERT, XLS-R, and Whis-per, to identify acoustic patterns related to de-pression directly from raw audio. Our methodcombines these models through ensembling tocapture different acoustic features. The ex-periments use stratified evaluation and cross-lingual analysis to check how well the mod-els work across languages. Results show thatpretrained acoustic representations effectivelycapture vocal features of depression, achiev-ing Macro-F1 scores of 0.9058 for Tamil and0.9396 for Malayalam. However, cross-lingualtransfer faces challenges because of phoneticand prosodic differences.