LLM evaluations using clinician-authored triage vignettes have reported substantial under-triage under constrained multiple-choice testing. Yet model performance on the same clinical cases can change when responses are generated in free text. We test whether this format effect appears while the case is processed or when clinical information is mapped to the final answer. Using sparse-autoencoder (SAE) features in Gemma 3 4B/12B IT and Qwen3-8B, we find that medical features fire on the shared clinical narrative under both formats but are inactive at the multiple-choice decision token. Emergency-tier information is linearly decodable from vignette representations with ROC-AUC 0.95–1.00 under both formats, with no significant format difference, but is attenuated at the decision token. Natural-language autoencoder verbalization and top-feature characterization associate that token with the multiple-choice scaffold. In a direct linear projection, the identified medical features contribute zero, whereas scaffold-peaking features account for over 91% of unsigned attribution in both Gemma models. Behaviorally, whether multiple choice improves or worsens performance depends on the model. Option-order shuffles rule out simple positional bias, and cases that differ between formats are usually one severity tier apart. Together, these findings place the strongest correlates of the format effect at answer selection while leaving open whether unmeasured clinical representations also differ.

Abstractive summarization of medical dialogues presents a challenge for standard training approaches, given the paucity of suitable datasets. We explore the performance of state-of-the-art models with zero-shot and few-shot learning strategies and measure the impact of pretraining with general domain and dialogue-specific text on the summarization performance.