This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

This systematic review and meta-analysis aimed to evaluate the impact of prospective payment systems (PPSs) on cholecystectomy.A comprehensive literature review was conducted, examining studies published until December 2023. The review process focused on identifying research across major databases that reported critical outcomes such as length of stay (LOS), mortality, complications, admissions, readmissions, and costs following PPS for cholecystectomy. The studies were specifically selected for their relevance to the impact of PPS or the transition from fee-for-service (FFS) to PPS. The study analyzed six papers, with three eligible for meta-analysis, to assess the impact of the shift from FFS to PPS in laparoscopic and open cholecystectomy procedures. Our findings indicated no significant changes in LOS and mortality rates following the transition from FFS to PPS. Complication rates varied and were influenced by the diagnosis-related group categorization and surgeon cost profiles under episode-based payment. There was a slight increase in admissions and readmissions, and mixed effects on hospital costs and financial margins, suggesting varied responses to PPS for cholecystectomy procedures. The impact of PPS on cholecystectomy is nuanced and varies across different aspects of healthcare delivery.Our findings indicate a need for adaptable, patient-centered PPS models that balance economic efficiency with high-quality patient care. The study emphasizes the importance of considering specific surgical procedures and patient demographics in healthcare payment reforms.

This systematic review and meta-analysis aimed to evaluate the impact of prospective payment systems (PPSs) on cholecystectomy.A comprehensive literature review was conducted, examining studies published until December 2023. The review process focused on identifying research across major databases that reported critical outcomes such as length of stay (LOS), mortality, complications, admissions, readmissions, and costs following PPS for cholecystectomy. The studies were specifically selected for their relevance to the impact of PPS or the transition from fee-for-service (FFS) to PPS. The study analyzed six papers, with three eligible for meta-analysis, to assess the impact of the shift from FFS to PPS in laparoscopic and open cholecystectomy procedures. Our findings indicated no significant changes in LOS and mortality rates following the transition from FFS to PPS. Complication rates varied and were influenced by the diagnosis-related group categorization and surgeon cost profiles under episode-based payment. There was a slight increase in admissions and readmissions, and mixed effects on hospital costs and financial margins, suggesting varied responses to PPS for cholecystectomy procedures. The impact of PPS on cholecystectomy is nuanced and varies across different aspects of healthcare delivery.Our findings indicate a need for adaptable, patient-centered PPS models that balance economic efficiency with high-quality patient care. The study emphasizes the importance of considering specific surgical procedures and patient demographics in healthcare payment reforms.

This systematic review and meta-analysis aimed to evaluate the impact of prospective payment systems (PPSs) on cholecystectomy.A comprehensive literature review was conducted, examining studies published until December 2023. The review process focused on identifying research across major databases that reported critical outcomes such as length of stay (LOS), mortality, complications, admissions, readmissions, and costs following PPS for cholecystectomy. The studies were specifically selected for their relevance to the impact of PPS or the transition from fee-for-service (FFS) to PPS. The study analyzed six papers, with three eligible for meta-analysis, to assess the impact of the shift from FFS to PPS in laparoscopic and open cholecystectomy procedures. Our findings indicated no significant changes in LOS and mortality rates following the transition from FFS to PPS. Complication rates varied and were influenced by the diagnosis-related group categorization and surgeon cost profiles under episode-based payment. There was a slight increase in admissions and readmissions, and mixed effects on hospital costs and financial margins, suggesting varied responses to PPS for cholecystectomy procedures. The impact of PPS on cholecystectomy is nuanced and varies across different aspects of healthcare delivery.Our findings indicate a need for adaptable, patient-centered PPS models that balance economic efficiency with high-quality patient care. The study emphasizes the importance of considering specific surgical procedures and patient demographics in healthcare payment reforms.

This systematic review and meta-analysis aimed to evaluate the impact of prospective payment systems (PPSs) on cholecystectomy.A comprehensive literature review was conducted, examining studies published until December 2023. The review process focused on identifying research across major databases that reported critical outcomes such as length of stay (LOS), mortality, complications, admissions, readmissions, and costs following PPS for cholecystectomy. The studies were specifically selected for their relevance to the impact of PPS or the transition from fee-for-service (FFS) to PPS. The study analyzed six papers, with three eligible for meta-analysis, to assess the impact of the shift from FFS to PPS in laparoscopic and open cholecystectomy procedures. Our findings indicated no significant changes in LOS and mortality rates following the transition from FFS to PPS. Complication rates varied and were influenced by the diagnosis-related group categorization and surgeon cost profiles under episode-based payment. There was a slight increase in admissions and readmissions, and mixed effects on hospital costs and financial margins, suggesting varied responses to PPS for cholecystectomy procedures. The impact of PPS on cholecystectomy is nuanced and varies across different aspects of healthcare delivery.Our findings indicate a need for adaptable, patient-centered PPS models that balance economic efficiency with high-quality patient care. The study emphasizes the importance of considering specific surgical procedures and patient demographics in healthcare payment reforms.