Title: The Efficacy and Comparison of Upper Gastrointestinal Bleeding Risk Scoring Systems
on Predicting Clinical Outcomes Among Emergency Unit Patients
Version: 1 Date: 06 Jan 2025
Reviewer:  Jayanton Patumanond
Reviewer's report:
This study involves prognostic prediction research (temporal and geographical external
validation phase) using a retrospective cohort design.
Four scores were selected, and five prediction endpoints were defined a priori. Since these five
prediction endpoints may or may not be highly correlated, this should be considered or accounted
for in the analysis.
Missing data (if any) should be reported, and the methods used to address missing data should
be clearly stated.
Pearson’s r is conceptually appropriate for continuous x and y variables, both with presumed
normal distributions. However, in this case, the y variables (the five predicted endpoints) are
binary. Therefore, Pearson’s r may be inappropriate. Moreover, testing whether these Pearson
r values are significantly different from zero is not meaningful in a clinical context and is
unnecessary.
If the authors wish to express the magnitude of explanation of the four scores over the five
predicted endpoints, it may be more appropriate to use the adjusted R-square value obtained
from simple logistic regression instead.
Measures of discrimination or classification of the scores, as presented with the area under the
receiver operating characteristic curve (AuROC), are descriptive by nature. Testing whether
the AuROC is significantly different from 0.5 (no discriminative power) is not meaningful
in a clinical context. Instead, the 95% confidence intervals of the AuROC values are already
sufficiently informative. Thus, it is unnecessary to present p-values for each AuROC.
Comparing the AuROC values among the four scores is more essential in this context.
For this purpose, the authors could use an online application (https://www.medcalc.org/
manual/comparison-of-roc-curves.php) or the “roccomp” command in Stata software (https://
www.stata.com/manuals/rroccomp.pdf). These tools perform a global test of differences in
AuROC among the four scores. If a significant difference is detected, multiple pairwise
comparisons may be conducted as appropriate.
When the AuROC, sensitivity, and/or specificity values are as low as 60%, even if they are the
highest values among the scores, interpreting them as “most useful” could be misleading and
should be avoided.
Title: The Efficacy and Comparison of Upper Gastrointestinal Bleeding Risk Scoring Systems
on Predicting Clinical Outcomes Among Emergency Unit Patients
Version: 1 Date: 06 Jan 2025
Reviewer:  Jayanton Patumanond
Reviewer's report:
This study involves prognostic prediction research (temporal and geographical external
validation phase) using a retrospective cohort design.
Four scores were selected, and five prediction endpoints were defined a priori. Since these five
prediction endpoints may or may not be highly correlated, this should be considered or accounted
for in the analysis.
Missing data (if any) should be reported, and the methods used to address missing data should
be clearly stated.
Pearson’s r is conceptually appropriate for continuous x and y variables, both with presumed
normal distributions. However, in this case, the y variables (the five predicted endpoints) are
binary. Therefore, Pearson’s r may be inappropriate. Moreover, testing whether these Pearson
r values are significantly different from zero is not meaningful in a clinical context and is
unnecessary.
If the authors wish to express the magnitude of explanation of the four scores over the five
predicted endpoints, it may be more appropriate to use the adjusted R-square value obtained
from simple logistic regression instead.
Measures of discrimination or classification of the scores, as presented with the area under the
receiver operating characteristic curve (AuROC), are descriptive by nature. Testing whether
the AuROC is significantly different from 0.5 (no discriminative power) is not meaningful
in a clinical context. Instead, the 95% confidence intervals of the AuROC values are already
sufficiently informative. Thus, it is unnecessary to present p-values for each AuROC.
Comparing the AuROC values among the four scores is more essential in this context.
For this purpose, the authors could use an online application (https://www.medcalc.org/
manual/comparison-of-roc-curves.php) or the “roccomp” command in Stata software (https://
www.stata.com/manuals/rroccomp.pdf). These tools perform a global test of differences in
AuROC among the four scores. If a significant difference is detected, multiple pairwise
comparisons may be conducted as appropriate.
When the AuROC, sensitivity, and/or specificity values are as low as 60%, even if they are the
highest values among the scores, interpreting them as “most useful” could be misleading and
should be avoided.
Please review this paper.
user
Title: The Efficacy and Comparison of Upper Gastrointestinal Bleeding Risk Scoring Systems
on Predicting Clinical Outcomes Among Emergency Unit Patients
Version: 1 Date: 06 Jan 2025
Reviewer:  Jayanton Patumanond
Reviewer's report:
This study involves prognostic prediction research (temporal and geographical external
validation phase) using a retrospective cohort design.
Four scores were selected, and five prediction endpoints were defined a priori. Since these five
prediction endpoints may or may not be highly correlated, this should be considered or accounted
for in the analysis.
Missing data (if any) should be reported, and the methods used to address missing data should
be clearly stated.
Pearson’s r is conceptually appropriate for continuous x and y variables, both with presumed
normal distributions. However, in this case, the y variables (the five predicted endpoints) are
binary. Therefore, Pearson’s r may be inappropriate. Moreover, testing whether these Pearson
r values are significantly different from zero is not meaningful in a clinical context and is
unnecessary.
If the authors wish to express the magnitude of explanation of the four scores over the five
predicted endpoints, it may be more appropriate to use the adjusted R-square value obtained
from simple logistic regression instead.
Measures of discrimination or classification of the scores, as presented with the area under the
receiver operating characteristic curve (AuROC), are descriptive by nature. Testing whether
the AuROC is significantly different from 0.5 (no discriminative power) is not meaningful
in a clinical context. Instead, the 95% confidence intervals of the AuROC values are already
sufficiently informative. Thus, it is unnecessary to present p-values for each AuROC.
Comparing the AuROC values among the four scores is more essential in this context.
For this purpose, the authors could use an online application (https://www.medcalc.org/
manual/comparison-of-roc-curves.php) or the “roccomp” command in Stata software (https://
www.stata.com/manuals/rroccomp.pdf). These tools perform a global test of differences in
AuROC among the four scores. If a significant difference is detected, multiple pairwise
comparisons may be conducted as appropriate.
When the AuROC, sensitivity, and/or specificity values are as low as 60%, even if they are the
highest values among the scores, interpreting them as “most useful” could be misleading and
should be avoided.
assistant