Title: Risk prediction and effect evaluation of complicated appendicitis based on XGBoost
modeling
Version: 0 Date: 10 Mar 2025
Reviewer's report:
Dear Author
First of all, thank you for your work. You are expected to make some necessary revisions.
Revision requests are as follows:
1. Classical statistical models could have been explained in addition to machine learning, but
it is not necessary.
2. It is not fully understood how to use model estimates in clinical practice. Can you explain
it clearly?
3. Different methods can be tried in variable selection for the model, and their compatibility
with each other can be evaluated.
4. Positive and negative predictive values should also be included in the results. In addition,
logistic regression values should be included in the manuscript.
Best regards,
Title: Risk prediction and effect evaluation of complicated appendicitis based on XGBoost
modeling
Version: 0 Date: 10 Mar 2025
Reviewer's report:
Dear Author
First of all, thank you for your work. You are expected to make some necessary revisions.
Revision requests are as follows:
1. Classical statistical models could have been explained in addition to machine learning, but
it is not necessary.
2. It is not fully understood how to use model estimates in clinical practice. Can you explain
it clearly?
3. Different methods can be tried in variable selection for the model, and their compatibility
with each other can be evaluated.
4. Positive and negative predictive values should also be included in the results. In addition,
logistic regression values should be included in the manuscript.
Best regards,
Please review this paper.
user
Title: Risk prediction and effect evaluation of complicated appendicitis based on XGBoost
modeling
Version: 0 Date: 10 Mar 2025
Reviewer's report:
Dear Author
First of all, thank you for your work. You are expected to make some necessary revisions.
Revision requests are as follows:
1. Classical statistical models could have been explained in addition to machine learning, but
it is not necessary.
2. It is not fully understood how to use model estimates in clinical practice. Can you explain
it clearly?
3. Different methods can be tried in variable selection for the model, and their compatibility
with each other can be evaluated.
4. Positive and negative predictive values should also be included in the results. In addition,
logistic regression values should be included in the manuscript.
Best regards,
assistant