Opper

Context Reasoning

Context understanding and reasoning tasks test accurate answers grounded in provided context. This capability is essential for knowledge-base support bots, policy lookup systems, and internal knowledge Q&A applications. Models are evaluated on their ability to provide accurate answers that are properly grounded in the given context rather than hallucinating information.

Context Reasoning – gcp/gemini-2.0-flash-lite Task | Opper AI

Input Tokens

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Output Tokens

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Context

Input

Return the Re-examination of the patient admitted to the hospital 2025-05-20 at 09.10

Expected output

Temperature: 38.8°C, Pulse: 102 bpm, Respiratory Rate: 22/min, O2 Sat: 93% (room air), BP: 114/68, Appearance: Pale, visibly unwell, clutching left side with each cough, Auscultation: Clear crackles LLL (Left Lower Lobe), slightly diminished breath sounds in same area, Percussion: Dullness confirmed, CRP: 98 mg/L, CBC:, WBC: 13.7 x10⁹/L (elevated), Neutrophils: 9.6 x10⁹/L, ECG: Sinus tachycardia, no ischemia, Chest X-ray (STAT reading):, Infiltrate in left lower lobe, mild pleural effusion, Consistent with community-acquired pneumonia

Model output

The re-examination of Melisa Farrow admitted to the hospital on 2025-05-20 at 09:10 revealed a diagnosis of acute community-acquired pneumonia (CAP) in the left lower lobe.