Title: Efficient diagnosis for endoscopic remission in Crohn’s diseases by the combination of
three non-invasive markers
Version: 3 Date: 04 Feb 2025
Reviewer's report:
### Reviewer’s Comments:
1. **Explanation Needed for ROC-Derived Cut-off Values**
Please explain why the ROC-derived cut-off values in this study differ significantly from the
AGA-recommended cut-off values.
2. **Data Description Following Shapiro–Wilk Test**
Please clarify the method of data description for variables that were found to be non-normally
distributed following the Shapiro–Wilk test.
3. **Clarification on Figure 2**
In Figure 2, what do the numbers in brackets, such as 81.5% (74.5–88.5), represent? Are these
confidence intervals, ranges, or interquartile ranges?
4. **Dissatisfaction with Response on Comment 3 (why adopted SVM)**
The response regarding the choice of SVM and the included variables is not satisfactory. Given
that the continuous variables LRG, CRP, and Fcal were transformed into binary categorical
variables (negative or positive), a decision tree algorithm might have been more effective. Please
provide a rationale for choosing SVM over other methods.
5. **Detailed Explanation of SVM Model Parameters**
Please provide a detailed description of the parameter selection process for the SVM model,
including the type of kernel function used and the optimization method for the penalty
parameter. Additionally, it is recommended to include a detailed list of model parameters in the
supplementary materials.
Title: Efficient diagnosis for endoscopic remission in Crohn’s diseases by the combination of
three non-invasive markers
Version: 3 Date: 04 Feb 2025
Reviewer's report:
### Reviewer’s Comments:
1. **Explanation Needed for ROC-Derived Cut-off Values**
Please explain why the ROC-derived cut-off values in this study differ significantly from the
AGA-recommended cut-off values.
2. **Data Description Following Shapiro–Wilk Test**
Please clarify the method of data description for variables that were found to be non-normally
distributed following the Shapiro–Wilk test.
3. **Clarification on Figure 2**
In Figure 2, what do the numbers in brackets, such as 81.5% (74.5–88.5), represent? Are these
confidence intervals, ranges, or interquartile ranges?
4. **Dissatisfaction with Response on Comment 3 (why adopted SVM)**
The response regarding the choice of SVM and the included variables is not satisfactory. Given
that the continuous variables LRG, CRP, and Fcal were transformed into binary categorical
variables (negative or positive), a decision tree algorithm might have been more effective. Please
provide a rationale for choosing SVM over other methods.
5. **Detailed Explanation of SVM Model Parameters**
Please provide a detailed description of the parameter selection process for the SVM model,
including the type of kernel function used and the optimization method for the penalty
parameter. Additionally, it is recommended to include a detailed list of model parameters in the
supplementary materials.
Please review this paper.
user
Title: Efficient diagnosis for endoscopic remission in Crohn’s diseases by the combination of
three non-invasive markers
Version: 3 Date: 04 Feb 2025
Reviewer's report:
### Reviewer’s Comments:
1. **Explanation Needed for ROC-Derived Cut-off Values**
Please explain why the ROC-derived cut-off values in this study differ significantly from the
AGA-recommended cut-off values.
2. **Data Description Following Shapiro–Wilk Test**
Please clarify the method of data description for variables that were found to be non-normally
distributed following the Shapiro–Wilk test.
3. **Clarification on Figure 2**
In Figure 2, what do the numbers in brackets, such as 81.5% (74.5–88.5), represent? Are these
confidence intervals, ranges, or interquartile ranges?
4. **Dissatisfaction with Response on Comment 3 (why adopted SVM)**
The response regarding the choice of SVM and the included variables is not satisfactory. Given
that the continuous variables LRG, CRP, and Fcal were transformed into binary categorical
variables (negative or positive), a decision tree algorithm might have been more effective. Please
provide a rationale for choosing SVM over other methods.
5. **Detailed Explanation of SVM Model Parameters**
Please provide a detailed description of the parameter selection process for the SVM model,
including the type of kernel function used and the optimization method for the penalty
parameter. Additionally, it is recommended to include a detailed list of model parameters in the
supplementary materials.
assistant