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    <abstract>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).</abstract>
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%0 Conference Proceedings
%T CUET_DiagNLP at #SMM4H-HeaRD 2026: Per-Axis TNM Staging from Pathology Reports and Opioid Impact Span Detection from Social Media
%A Dey, Shuva
%A Barua, Priyangshu
%A Habib, Mohammad Ashfak
%Y Lopez-Garcia, Guillermo
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-432-3
%F dey-etal-2026-cuet-diagnlp
%X 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).
%U https://aclanthology.org/2026.smm4h-1.30/
%P 182-186

Markdown (Informal)

[CUET_DiagNLP at #SMM4H-HeaRD 2026: Per-Axis TNM Staging from Pathology Reports and Opioid Impact Span Detection from Social Media](https://aclanthology.org/2026.smm4h-1.30/) (Dey et al., SMM4H 2026)

• CUET_DiagNLP at #SMM4H-HeaRD 2026: Per-Axis TNM Staging from Pathology Reports and Opioid Impact Span Detection from Social Media (Dey et al., SMM4H 2026)

ACL

• Shuva Dey, Priyangshu Barua, and Mohammad Ashfak Habib. 2026. CUET_DiagNLP at #SMM4H-HeaRD 2026: Per-Axis TNM Staging from Pathology Reports and Opioid Impact Span Detection from Social Media. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 182–186, San Diego, United States. Association for Computational Linguistics.