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Dyslipidemia constitutes an essential risk factor for atherosclerotic disorders. From employing diverse types of blood lipid indicators as markers for risk prediction to subsequently applying low-density lipoprotein cholesterol as the primary lipid-lowering target in both primary and secondary prevention of cardiovascular diseases, relevant research and clinical practices indicate that the dyslipidemia management system grounded on hierarchical medical care is a significant measure for effective blood lipid control and efficient reduction of cardiovascular diseases. Continuously optimizing the prediction model for cardiovascular diseases, enhancing the construction of the medical consortium specializing in dyslipidemia disorders, and promoting the dyslipidemia management and control system based on hierarchical medical treatment will facilitate elevating the overall prevention and treatment level of dyslipidemia in China, and mitigate the health menace of cardiovascular diseases for Chinese residents.

The hierarchical medical care structure is an efficient means of addressing some inadequate medical capabilities. Evaluating the several degrees of disease based on the physician’s diagnosis is a vital step in building a hierarchical healthcare treatment strategy. This study aims to offer different dynamic T-spherical fuzzy aggregation operators (AOs) for addressing multi-period decision-making (MPDM) situations in which decision-makers (DMs) have access to all information in the form of T-spherical fuzzy numbers (T-SFNs) spanning many time periods. AOs play a crucial role in decision-making, particularly when competing considerations are present. Developing suitable solutions for the aggregation process is a crucial challenge when dealing with unclear data. ”T-spherical fuzzy dynamic Einstein weighted averaging (T-SFDEWA) operator and the T-spherical fuzzy dynamic Einstein weighted geometric (T-SFDEWG) operator” are two innovative Einstein AOs. Several appealing properties of these AOs are addressed in depth. In addition, we present a strategy for addressing MPDM difficulties using optimum solutions. A numerical illustration of a hierarchical medical care system is provided to illustrate how the suggested method could be implemented. An authenticity assessment and comparative analysis is also provided to illustrate the effectiveness of the suggested method. In practice, the proposed AOs and decision-making technique are typically useful in multi-stage and dynamic decision analysis.

The hierarchical medical care structure is an efficient means of addressing some inadequate medical capabilities. Evaluating the several degrees of disease based on the physician's diagnosis is a vital step in building a hierarchical healthcare treatment strategy. This study aims to offer different dynamic T-spherical fuzzy aggregation operators (AOs) for addressing multi-period decision-making (MPDM) situations in which decision-makers (DMs) have access to all information in the form of T-spherical fuzzy numbers (T-SFNs) spanning many time periods. AOs play a crucial role in decision-making, particularly when competing considerations are present. Developing suitable solutions for the aggregation process is a crucial challenge when dealing with unclear data. "T-spherical fuzzy dynamic Einstein weighted averaging (T-SFDEWA) operator and the T-spherical fuzzy dynamic Einstein weighted geometric (T-SFDEWG) operator" are two innovative Einstein AOs. Several appealing properties of these AOs are addressed in depth. In addition, we present a strategy for addressing MPDM difficulties using optimum solutions. A numerical illustration of a hierarchical medical care system is provided to illustrate how the suggested method could be implemented. An authenticity assessment and comparative analysis is also provided to illustrate the effectiveness of the suggested method. In practice, the proposed AOs and decision-making technique are typically useful in multi-stage and dynamic decision analysis. [ABSTRACT FROM AUTHOR]

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