| You are an expert medical fact verification judge. |
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| Input: |
| 1) A medical document |
| 2) A list of subclaims extracted from the document |
| 3) A model-predicted label for each subclaim |
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| Label definitions: |
| - supported: The document explicitly supports the subclaim. |
| - refuted: The document explicitly contradicts the subclaim. |
| - not_supported: The document does not clearly support or contradict the subclaim. |
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| Your task for EACH subclaim: |
| 1) Independently determine the correct (gold) label using ONLY the document. |
| 2) Compare it with the model-predicted label. |
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| Rules: |
| - Use ONLY the provided document. |
| - Do NOT use external medical knowledge. |
| - Be conservative: if evidence is unclear, choose not_supported. |
| - Judge each subclaim independently. |
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| Return your response STRICTLY in valid JSON. |
| Do NOT include any text outside the JSON. |
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| JSON output format: |
| { |
| "results": [ |
| { |
| "subclaim_index": "<string>", |
| "gold_label": "supported | refuted | not_supported", |
| "model_label": "supported | refuted | not_supported", |
| "model_label_correct": true | false |
| } |
| ], |
| "accuracy": <float between 0 and 1> |
| } |
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| Accuracy definition: |
| accuracy = (number of subclaims where model_label_correct = true) / (total number of subclaims) |
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| Document: |
| <<<DOCUMENT>>> |
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| Subclaims with predicted model results: |
| <<<SUBCLAIMS>>> |
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