Title: Changes in the salivary metabolome in patients with chronic erosive gastritis
Version: 2 Date: 05 Jan 2023
Reviewer's report:
The authors have applied various statistical tests to screen potential biomarkers that can identify
diseases from saliva samples of CEG patients using metabolomics.
I do not have any major concerns except one.
My only concern is that the method part does not address the issue of imbalanced class design.
Please refer to Section 3 (Results).
“Among 64 patients in this study, there were 31 females and 33 males with an average age of
49.64 ± 6.80 years old. Among the 30 participants in the healthy control group, there were 14
males and 16 females”...where the study has 31 females and 33 males out of n=64. In the next
step among the healthy control participants, 14 were males and 16 females.
This is a balanced class design which might create bias towards a particular set of findings,
for example, important/potential predictors in saliva of CEG patients.  Since, the authors are
using  Receiver operating characteristic (ROC) to determine potential predictors in saliva of
CEG patients, I would like to check if there are significant differences (or no difference) in
identifying the potential predictors when there is an imbalanced class.
Imbalance class example: out of n=64 patients, 10 are males and 54 are females. Subsequently,
out of 30 healthy control participants- 5 females and 25 males.
Title: Changes in the salivary metabolome in patients with chronic erosive gastritis
Version: 2 Date: 05 Jan 2023
Reviewer's report:
The authors have applied various statistical tests to screen potential biomarkers that can identify
diseases from saliva samples of CEG patients using metabolomics.
I do not have any major concerns except one.
My only concern is that the method part does not address the issue of imbalanced class design.
Please refer to Section 3 (Results).
“Among 64 patients in this study, there were 31 females and 33 males with an average age of
49.64 ± 6.80 years old. Among the 30 participants in the healthy control group, there were 14
males and 16 females”...where the study has 31 females and 33 males out of n=64. In the next
step among the healthy control participants, 14 were males and 16 females.
This is a balanced class design which might create bias towards a particular set of findings,
for example, important/potential predictors in saliva of CEG patients.  Since, the authors are
using  Receiver operating characteristic (ROC) to determine potential predictors in saliva of
CEG patients, I would like to check if there are significant differences (or no difference) in
identifying the potential predictors when there is an imbalanced class.
Imbalance class example: out of n=64 patients, 10 are males and 54 are females. Subsequently,
out of 30 healthy control participants- 5 females and 25 males.
Please review this paper.
user
Title: Changes in the salivary metabolome in patients with chronic erosive gastritis
Version: 2 Date: 05 Jan 2023
Reviewer's report:
The authors have applied various statistical tests to screen potential biomarkers that can identify
diseases from saliva samples of CEG patients using metabolomics.
I do not have any major concerns except one.
My only concern is that the method part does not address the issue of imbalanced class design.
Please refer to Section 3 (Results).
“Among 64 patients in this study, there were 31 females and 33 males with an average age of
49.64 ± 6.80 years old. Among the 30 participants in the healthy control group, there were 14
males and 16 females”...where the study has 31 females and 33 males out of n=64. In the next
step among the healthy control participants, 14 were males and 16 females.
This is a balanced class design which might create bias towards a particular set of findings,
for example, important/potential predictors in saliva of CEG patients.  Since, the authors are
using  Receiver operating characteristic (ROC) to determine potential predictors in saliva of
CEG patients, I would like to check if there are significant differences (or no difference) in
identifying the potential predictors when there is an imbalanced class.
Imbalance class example: out of n=64 patients, 10 are males and 54 are females. Subsequently,
out of 30 healthy control participants- 5 females and 25 males.
assistant