Title: Leveraging Machine Learning for Precision Medicine: A Predictive Model for Cognitive
Impairment in Cholestasis Patients
Version: 3 Date: 09 Jan 2025
Reviewer:  Dawei Ding
Reviewer's report:
Leveraging Machine Learning for Precision Medicine: A Predictive Model for Cognitive
Impairment in Cholestasis Patients
Comments for Transmission to the Authors
It is a retrospective, monocentric analysis of 353 patients with cholestasis. The statistical analysis
is largely adequate, and the conclusions are supported by the data provided.
Major Points:
1 In the introduction, the author describes that while MoCA has demonstrated utility in patients
with neurodegenerative diseases and cerebrovascular disorders, its application in hepatobiliary
conditions remains underexplored. The authors should demonstrate the legitimacy of MoCA for
assessing cognitive function in cholestatic conditions before using it as an alternative endpoint.
Have any other studies shown this before?
2 Which guidelines were used to diagnose cholestasis?
3 The discussion section may consider highlighting the uniqueness of this study by focusing on
characteristic indicators that predict cognitive function associated with cholestasis conditions
compared to other diseases (neurodegenerative diseases or cerebrovascular disorders).
Minor Points:
1 In the introduction, the author describes that studies focusing on cholestasis have primarily
been descriptive, detailing its association with hepatic encephalopathy or neuroinflammatory
states but lacking targeted tools for stratifying cognitive risk. Furthermore, existing predictive
tools for cognitive impairment often fail to account for the unique biochemical and metabolic
profiles of cholestasis patients, which include elevated bilirubin, altered coagulation parameters,
and systemic inflammation markers such as D-dimer. There was a lack of citations here.
2 Many punctuation marks in the article were in Chinese format, please correct them.
Title: Leveraging Machine Learning for Precision Medicine: A Predictive Model for Cognitive
Impairment in Cholestasis Patients
Version: 3 Date: 09 Jan 2025
Reviewer:  Dawei Ding
Reviewer's report:
Leveraging Machine Learning for Precision Medicine: A Predictive Model for Cognitive
Impairment in Cholestasis Patients
Comments for Transmission to the Authors
It is a retrospective, monocentric analysis of 353 patients with cholestasis. The statistical analysis
is largely adequate, and the conclusions are supported by the data provided.
Major Points:
1 In the introduction, the author describes that while MoCA has demonstrated utility in patients
with neurodegenerative diseases and cerebrovascular disorders, its application in hepatobiliary
conditions remains underexplored. The authors should demonstrate the legitimacy of MoCA for
assessing cognitive function in cholestatic conditions before using it as an alternative endpoint.
Have any other studies shown this before?
2 Which guidelines were used to diagnose cholestasis?
3 The discussion section may consider highlighting the uniqueness of this study by focusing on
characteristic indicators that predict cognitive function associated with cholestasis conditions
compared to other diseases (neurodegenerative diseases or cerebrovascular disorders).
Minor Points:
1 In the introduction, the author describes that studies focusing on cholestasis have primarily
been descriptive, detailing its association with hepatic encephalopathy or neuroinflammatory
states but lacking targeted tools for stratifying cognitive risk. Furthermore, existing predictive
tools for cognitive impairment often fail to account for the unique biochemical and metabolic
profiles of cholestasis patients, which include elevated bilirubin, altered coagulation parameters,
and systemic inflammation markers such as D-dimer. There was a lack of citations here.
2 Many punctuation marks in the article were in Chinese format, please correct them.
Please review this paper.
user
Title: Leveraging Machine Learning for Precision Medicine: A Predictive Model for Cognitive
Impairment in Cholestasis Patients
Version: 3 Date: 09 Jan 2025
Reviewer:  Dawei Ding
Reviewer's report:
Leveraging Machine Learning for Precision Medicine: A Predictive Model for Cognitive
Impairment in Cholestasis Patients
Comments for Transmission to the Authors
It is a retrospective, monocentric analysis of 353 patients with cholestasis. The statistical analysis
is largely adequate, and the conclusions are supported by the data provided.
Major Points:
1 In the introduction, the author describes that while MoCA has demonstrated utility in patients
with neurodegenerative diseases and cerebrovascular disorders, its application in hepatobiliary
conditions remains underexplored. The authors should demonstrate the legitimacy of MoCA for
assessing cognitive function in cholestatic conditions before using it as an alternative endpoint.
Have any other studies shown this before?
2 Which guidelines were used to diagnose cholestasis?
3 The discussion section may consider highlighting the uniqueness of this study by focusing on
characteristic indicators that predict cognitive function associated with cholestasis conditions
compared to other diseases (neurodegenerative diseases or cerebrovascular disorders).
Minor Points:
1 In the introduction, the author describes that studies focusing on cholestasis have primarily
been descriptive, detailing its association with hepatic encephalopathy or neuroinflammatory
states but lacking targeted tools for stratifying cognitive risk. Furthermore, existing predictive
tools for cognitive impairment often fail to account for the unique biochemical and metabolic
profiles of cholestasis patients, which include elevated bilirubin, altered coagulation parameters,
and systemic inflammation markers such as D-dimer. There was a lack of citations here.
2 Many punctuation marks in the article were in Chinese format, please correct them.
assistant