We describe our system for the AbjadMed shared task on Arabic medical text classification at AbjadNLP 2026. Our approach combines efficient fine-tuning of Qwen3-8B using QLoRA with a Dice+CrossEntropy hybrid loss designed for Macro F1 optimization. Taking inspiration from recent research on optimal LoRA configurations, we apply low-rank adapters to all linear layers of the model rather than attention layers only, which we validate improves performance by 4.0 points. We also explore data augmentation through machine translation of external medical QA data, though this did not improve generalization. Our best submission achieves a Macro F1 score of 0.4441 on the test set.