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		<title>AI Tumor Board Passes Reproducibility Test: Structured Output Tames Sarcoma LLM Chaos</title>
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		<pubDate>Sun, 11 Oct 2026 01:36:33 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Claude Opus 4.5]]></category>
		<category><![CDATA[Clinical decision support]]></category>
		<category><![CDATA[hallucination]]></category>
		<category><![CDATA[Large Language Models]]></category>
		<category><![CDATA[multidisciplinary team]]></category>
		<category><![CDATA[Oncology]]></category>
		<category><![CDATA[Reproducibility]]></category>
		<category><![CDATA[sarcoma]]></category>
		<category><![CDATA[schema validation]]></category>
		<category><![CDATA[structured output]]></category>
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					<description><![CDATA[A schema-enforced large language model framework achieved 94 percent reproducibility across simulated sarcoma tumor board decisions, a leap over earlier free-text benchmarks, though the authors caution that consistent outputs are not necessarily correct ones.]]></description>
		
		
		
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