With the rapid development of medical big data and artificial intelligence,Clinical Prediction Models(CPMs)have become pivotal tools for disease prevention,diagnosis,and treatment.Current research predominantly focuses on the economic analysis of pharmacological or public health interventions,yet a comprehensive methodological framework for the economic evaluation of CPMs has been notably absent.The Guidelines for Economic Evaluation of Clinical Prediction Models(hereafter the Guidelines),jointly initiated by the Chinese Research Hospital Association and Peking University,Tsinghua University,and Capital Medical University,adheres to the WHO Handbook for Guideline Development and the Reporting Items for Practice Guidelines in Healthcare(RIGHT)standards.A multidisciplinary collaboration,including a steering committee,expert panel,secretariat,and external review group,was established to develop the guideline following evidence-based principles and procedures.Consensus recommendations were formulated through the Delphi method.It describes the background,objectives,target group,and the development methodology and process,ensuring the entire compilation process of the Guidelines is transparent and standardized.Through comprehensive evidence retrieval,systematic evidence appraisal,and a scientific approach to forming recommendations,the scientific rigor and validity of the Guidelines were further enhanced.

With the rapid development of medical big data and artificial intelligence,Clinical Prediction Models(CPMs)have become pivotal tools for disease prevention,diagnosis,and treatment.Current research predominantly focuses on the economic analysis of pharmacological or public health interventions,yet a comprehensive methodological framework for the economic evaluation of CPMs has been notably absent.The Guidelines for Economic Evaluation of Clinical Prediction Models(hereafter the Guidelines),jointly initiated by the Chinese Research Hospital Association and Peking University,Tsinghua University,and Capital Medical University,adheres to the WHO Handbook for Guideline Development and the Reporting Items for Practice Guidelines in Healthcare(RIGHT)standards.A multidisciplinary collaboration,including a steering committee,expert panel,secretariat,and external review group,was established to develop the guideline following evidence-based principles and procedures.Consensus recommendations were formulated through the Delphi method.It describes the background,objectives,target group,and the development methodology and process,ensuring the entire compilation process of the Guidelines is transparent and standardized.Through comprehensive evidence retrieval,systematic evidence appraisal,and a scientific approach to forming recommendations,the scientific rigor and validity of the Guidelines were further enhanced.

Objective To analyze tumor immune microenvironment and related mechanisms in liver cancer.Methods We included 10 cases of hepatocellular carcinoma,hepatitis B patients and healthy volunteers from January 2015 to December 2017 in Shanxi Grand Hospital.We first detected the peripheral and local GM-CSF level in each group,detected myeloid-derived suppressor cells (MDSCs) GM-CSF and pathway-related protein expression.from liver cancer,tumor margin and normal liver tissue through flow cytometry and immunohistochemistry,Finally,we transfected the CCR4-NOT transcriptional complex subunit 7 (CNOT7) recombinant plasmid in the hepatoma cell line,and then detected the related protein expression.Results There was no significant difference for peripheral blood GM-CSF level between liver cancer group,hepatitis group and control group (P＞0.05).The level of local GM-CSF was (32.2±8.9) ng/L,which was higher than that of hepatocellular carcinoma (9.7±2.7) ng/L and normal liver tissue (11.6±2.9) ng/L.The difference was statistically significant (P＜0.05).The proportion of MDSCs at the edge of the tumor was (9.9 ±3.6) ％,which was higher than that of liver cancer (4.0± 1.5) ％ and normal liver tissue (6.3±2.3) ％,and the difference was statistically significant (P＜0.05).Immunohistochemistrydata was consistent with previous data.Compared with normal liver tissue,CNOT7 and STAT3 were highly expressed in liver cancer tissues,while STAT1 was lowly expressed.HepG2 human hepatoma cells were selected for transfection.Compared with the empty plasmid group,CNOT7 expression was decreased in the knocking out group at the same time STAT1 expression was increased,STAT3 and GM-CSF expression was decreased.Conclusion In hepatocellular carcinoma,the secretion of GM-CSF increased and the number of MDSCs increased.Knocking out CNOT7 reduced GM-CSF secretion and activate the JAK/STAT signaling pathway.