Patients leave hospital with a discharge summary written for clinicians: "Furosemide 40 mg PO BID (INCREASED from 20 mg daily)", "hold if heart rate below 55". Many of them read English as a second language. A language model can rewrite and translate that page in seconds, and it can also change a dose, drop a "stop taking", or swap a phone number, and nobody at the bedside can proofread a sheet in Ukrainian or Arabic. Clearsheet keeps the model in the drafting seat. Identifiers are masked on the hospital side first. Gemma 3 27B (bf16, one AMD Instinct MI300X, vLLM on ROCm) extracts medicines, follow-ups and warning signs, each with a verbatim source quote, then writes a plain-English line, the patient's language, and a literal back-translation. Deterministic code checks that every quote exists in the document, that every number sits inside its quote with its unit, that translations keep the dose, drug name and emergency action, and sends anticoagulants, insulin, opioids, stops and dose changes to a nurse. The nurse review console shows each line next to a highlighter mark on its source sentence; nothing prints until every flagged line has a decision. The printed sheet puts the patient's language first, English underneath, medicines as a pill-box grid. On 8 synthetic cases in 6 languages: 97% of medicines found, 100% exact doses, 0 invented medicines, 99% of planted wrong numbers blocked. One real catch: Gemma translated "call 911" as "call 103", Ukraine's ambulance number. Batched on one MI300X: 1118 output tokens/s.
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