Recent Large Audio-Language Models (LALMs) integrate acoustic capabilities into reasoning, yet whether they reliably ground clinical judgments in audible evidence remains unproven. We introduce CliniCAST (Clinical Controlled Acoustic Synthetic Triage), a controlled benchmark that disentangles clinically meaningful acoustic cues from lexical content and speaker demographics. CliniCAST comprises 5,856 synthetic samples across 12 disease conditions: 4,800 audio samples forming 2,400 tagged–untagged pairs for five-level emergency triage, and 1,056 audio–text inconsistent samples in which reassuring speech is paired with high-risk acoustic cues. Evaluating a diverse suite of audio-capable foundation models, we find that LALMs exhibit fragile acoustic grounding and a pronounced “text dominance” failure mode: reassuring lexical content suppresses response to audible distress signals even under safety-critical conditions. Age and gender interactions are weak across conditions, indicating that the primary failure mode is insufficient cross-modal integration rather than demographic bias. These results suggest current LALMs are not yet robust enough for high-stakes medical triage, and motivate training objectives that explicitly enforce reliance on clinically grounded audible evidence.