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		<title>Nationwide cross-sectional survey finds varied pricing relationships in 63 fixed-dose and free-equivalent groups in China’s NRDL</title>
		<link>https://bioengineer.org/nationwide-cross-sectional-survey-finds-varied-pricing-relationships-in-63-fixed-dose-and-free-equivalent-groups-in-chinas-nrdl/</link>
		
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		<pubDate>Tue, 20 Oct 2026 21:03:48 +0000</pubDate>
				<category><![CDATA[Health]]></category>
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		<category><![CDATA[China]]></category>
		<category><![CDATA[fixed-dose combinations]]></category>
		<category><![CDATA[free-equivalent combinations]]></category>
		<category><![CDATA[health services research]]></category>
		<category><![CDATA[medicine prices]]></category>
		<category><![CDATA[National Reimbursement Drug List]]></category>
		<category><![CDATA[prices]]></category>
		<category><![CDATA[reimbursement category]]></category>
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					<description><![CDATA[A nationwide cross-sectional study of 63 FDC–FEC groups found a median fixed-dose-to-free-equivalent unit-price ratio of 0.94, with differences associated with therapeutic area and reimbursement category.]]></description>
		
		
		
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		<title>Ridge regression outperforms nonlinear models for petrol price prediction in regulated Zambia</title>
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		<pubDate>Tue, 20 Oct 2026 20:57:55 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[cost-plus pricing]]></category>
		<category><![CDATA[exchange rates]]></category>
		<category><![CDATA[Explainable AI]]></category>
		<category><![CDATA[fuel regulation]]></category>
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		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[petrol]]></category>
		<category><![CDATA[petrol prices]]></category>
		<category><![CDATA[Predicting]]></category>
		<category><![CDATA[prices]]></category>
		<category><![CDATA[ridge regression]]></category>
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					<description><![CDATA[A machine-learning study using 2011–2025 data from Zambia’s regulated fuel-pricing system found that Ridge regression outperformed Random Forest, Support Vector Regression and LSTM in conditional petrol price prediction, while explainable analysis identified international oil prices and exchange rates as the main predictive drivers.]]></description>
		
		
		
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