Title: Efficient diagnosis for endoscopic remission in Crohn’s diseases by the combination of
three non-invasive markers
Version: 2 Date: 31 Dec 2024
Reviewer's report:
Endoscopic assessments are inconvenient for many IBD patients. Therefore, this manuscript,
which investigates the value of non-invasive biomarkers for detecting endoscopic remission, is
highly relevant to the field.
I have the following remarks for the authors:
1- The manuscript frequently uses the term “predict.” However, “predict” implies that biomarker
values are measured at a time point preceding the endoscopic assessments. Since the biomarkers
in this study are measured simultaneously with the endoscopic assessments, it would be more
appropriate to use the term “monitor”.
2- What statistical tests were used to compare AUC, sensitivity, and other metrics between single
biomarkers, combinations of biomarkers, or the two-step approach?
3- It is unclear why the authors chose to use Support Vector Machines or which variables were
included as inputs in this machine learning process. Additionally, the manuscript refers to K-
fold cross-validation as a “machine learning” method, but it is actually a “resampling” method.
- To address points 2 and 3, the authors could select an appropriate statistical test to
compare p-values. Furthermore, robust resampling methods, such as K-fold cross-validation or
bootstrapping, should be employed to estimate uncertainty in confidence intervals.
4- Were CDAI values available at all endoscopic time points?
Since the study aims to investigate the role of “non-invasive” tools in monitoring endoscopic
disease activity, I recommend including clinical disease activity measures in the comparison.
Assessing clinical symptoms is even easier than obtaining the three biomarkers evaluated in this
study and could provide additional context.
Title: Efficient diagnosis for endoscopic remission in Crohn’s diseases by the combination of
three non-invasive markers
Version: 2 Date: 31 Dec 2024
Reviewer's report:
Endoscopic assessments are inconvenient for many IBD patients. Therefore, this manuscript,
which investigates the value of non-invasive biomarkers for detecting endoscopic remission, is
highly relevant to the field.
I have the following remarks for the authors:
1- The manuscript frequently uses the term “predict.” However, “predict” implies that biomarker
values are measured at a time point preceding the endoscopic assessments. Since the biomarkers
in this study are measured simultaneously with the endoscopic assessments, it would be more
appropriate to use the term “monitor”.
2- What statistical tests were used to compare AUC, sensitivity, and other metrics between single
biomarkers, combinations of biomarkers, or the two-step approach?
3- It is unclear why the authors chose to use Support Vector Machines or which variables were
included as inputs in this machine learning process. Additionally, the manuscript refers to K-
fold cross-validation as a “machine learning” method, but it is actually a “resampling” method.
- To address points 2 and 3, the authors could select an appropriate statistical test to
compare p-values. Furthermore, robust resampling methods, such as K-fold cross-validation or
bootstrapping, should be employed to estimate uncertainty in confidence intervals.
4- Were CDAI values available at all endoscopic time points?
Since the study aims to investigate the role of “non-invasive” tools in monitoring endoscopic
disease activity, I recommend including clinical disease activity measures in the comparison.
Assessing clinical symptoms is even easier than obtaining the three biomarkers evaluated in this
study and could provide additional context.
Please review this paper.
user
Title: Efficient diagnosis for endoscopic remission in Crohn’s diseases by the combination of
three non-invasive markers
Version: 2 Date: 31 Dec 2024
Reviewer's report:
Endoscopic assessments are inconvenient for many IBD patients. Therefore, this manuscript,
which investigates the value of non-invasive biomarkers for detecting endoscopic remission, is
highly relevant to the field.
I have the following remarks for the authors:
1- The manuscript frequently uses the term “predict.” However, “predict” implies that biomarker
values are measured at a time point preceding the endoscopic assessments. Since the biomarkers
in this study are measured simultaneously with the endoscopic assessments, it would be more
appropriate to use the term “monitor”.
2- What statistical tests were used to compare AUC, sensitivity, and other metrics between single
biomarkers, combinations of biomarkers, or the two-step approach?
3- It is unclear why the authors chose to use Support Vector Machines or which variables were
included as inputs in this machine learning process. Additionally, the manuscript refers to K-
fold cross-validation as a “machine learning” method, but it is actually a “resampling” method.
- To address points 2 and 3, the authors could select an appropriate statistical test to
compare p-values. Furthermore, robust resampling methods, such as K-fold cross-validation or
bootstrapping, should be employed to estimate uncertainty in confidence intervals.
4- Were CDAI values available at all endoscopic time points?
Since the study aims to investigate the role of “non-invasive” tools in monitoring endoscopic
disease activity, I recommend including clinical disease activity measures in the comparison.
Assessing clinical symptoms is even easier than obtaining the three biomarkers evaluated in this
study and could provide additional context.
assistant