Title: The best Anthropometric indices to predict colon cancer
Version: 1 Date: 01 Mar 2025
Reviewer's report:
1. I wanted to bring to your attention that the graphs in the article are provided in HEIC format,
which is not easily accessible for all users. As a reader, I found it challenging to open and view
these graphs. Therefore, in response to the review, please upload the graphs in a more accessible
formats such as PNG or JPEG .
2. Were continuous variables in Table 1 checked for normality? Did you use appropriate tests
for comparison between groups based on normality or skewness? Although the large sample
size can help approximate normality, checking for normality and considering non-parametric
tests can still be beneficial.
3. What percentage of your data was imputed, and can you provide a reference showing that this
percentage does not increase bias in your results?
4. Please complete the footnotes of the tables with abbreviations (e.g., M, SD, BMI, WC, etc.).
5. Please complete the titles of the AUC results tables by adding the response of colon
cancer. Each table should be self-explanatory without the need for textual interpretation, clearly
indicating which variables are being examined and through which model.
6. Correct typographical errors and ensure proper spacing throughout the text.
Title: The best Anthropometric indices to predict colon cancer
Version: 1 Date: 01 Mar 2025
Reviewer's report:
1. I wanted to bring to your attention that the graphs in the article are provided in HEIC format,
which is not easily accessible for all users. As a reader, I found it challenging to open and view
these graphs. Therefore, in response to the review, please upload the graphs in a more accessible
formats such as PNG or JPEG .
2. Were continuous variables in Table 1 checked for normality? Did you use appropriate tests
for comparison between groups based on normality or skewness? Although the large sample
size can help approximate normality, checking for normality and considering non-parametric
tests can still be beneficial.
3. What percentage of your data was imputed, and can you provide a reference showing that this
percentage does not increase bias in your results?
4. Please complete the footnotes of the tables with abbreviations (e.g., M, SD, BMI, WC, etc.).
5. Please complete the titles of the AUC results tables by adding the response of colon
cancer. Each table should be self-explanatory without the need for textual interpretation, clearly
indicating which variables are being examined and through which model.
6. Correct typographical errors and ensure proper spacing throughout the text.
Please review this paper.
user
Title: The best Anthropometric indices to predict colon cancer
Version: 1 Date: 01 Mar 2025
Reviewer's report:
1. I wanted to bring to your attention that the graphs in the article are provided in HEIC format,
which is not easily accessible for all users. As a reader, I found it challenging to open and view
these graphs. Therefore, in response to the review, please upload the graphs in a more accessible
formats such as PNG or JPEG .
2. Were continuous variables in Table 1 checked for normality? Did you use appropriate tests
for comparison between groups based on normality or skewness? Although the large sample
size can help approximate normality, checking for normality and considering non-parametric
tests can still be beneficial.
3. What percentage of your data was imputed, and can you provide a reference showing that this
percentage does not increase bias in your results?
4. Please complete the footnotes of the tables with abbreviations (e.g., M, SD, BMI, WC, etc.).
5. Please complete the titles of the AUC results tables by adding the response of colon
cancer. Each table should be self-explanatory without the need for textual interpretation, clearly
indicating which variables are being examined and through which model.
6. Correct typographical errors and ensure proper spacing throughout the text.
assistant