Title: Construction and validation of a predictive model for the risk of rebleeding in patients
with esophageal and gastric varices hemorrhage
Version: 3 Date: 04 Nov 2024
Reviewer's report:
This paper aims to construct and validate a predictive model for the risk of rebleeding in patients
with esophageal and gastric varices hemorrhage (EGVH) and compares it with existing scoring
systems such as GBS and pRS. Based on retrospective clinical data from 265 patients, the study
constructs a model using binary logistic regression and validates its predictive ability through
metrics like ROC curves and AUC. While the study holds clinical significance, several aspects
require improvement.
1. Methodology
Sample Size: This is a single-center retrospective study with a relatively small sample size (265
patients), which may impact the model’s external validity. It is recommended that future studies
include larger, multi-center prospective trials to further validate the model's stability and broader
applicability.
Factor Selection: The study excluded certain conventional factors, such as hemoglobin levels,
from the model and instead chose transfusion requirements as a predictive factor. While this is
justifiable, the rationale appears somewhat weak. It is recommended to provide more literature
support for this decision in the discussion.
2. Data Analysis
The paper handles data meticulously, using R software for data cleaning, feature selection, and
model validation, which is in line with standard statistical methods. However, some important
metrics, such as sensitivity (0.40), are relatively low, which may affect the model's practical
prediction performance. It is advised to explore ways to enhance the sensitivity of the model
while retaining its high specificity (0.936).
3. Discussion and Conclusion
The discussion section covers most relevant points, noting the limitations of existing scoring
systems and highlighting the advantages of the new model. However, there is not enough
in-depth discussion on the clinical applicability and future research directions, particularly
concerning the model’s adaptability and limitations across different patient populations or
conditions. It is suggested to expand on these points, especially on potential challenges in
applying the model in real-world settings.
My recommendation to this article is accept after minor revision.
Title: Construction and validation of a predictive model for the risk of rebleeding in patients
with esophageal and gastric varices hemorrhage
Version: 3 Date: 04 Nov 2024
Reviewer's report:
This paper aims to construct and validate a predictive model for the risk of rebleeding in patients
with esophageal and gastric varices hemorrhage (EGVH) and compares it with existing scoring
systems such as GBS and pRS. Based on retrospective clinical data from 265 patients, the study
constructs a model using binary logistic regression and validates its predictive ability through
metrics like ROC curves and AUC. While the study holds clinical significance, several aspects
require improvement.
1. Methodology
Sample Size: This is a single-center retrospective study with a relatively small sample size (265
patients), which may impact the model’s external validity. It is recommended that future studies
include larger, multi-center prospective trials to further validate the model's stability and broader
applicability.
Factor Selection: The study excluded certain conventional factors, such as hemoglobin levels,
from the model and instead chose transfusion requirements as a predictive factor. While this is
justifiable, the rationale appears somewhat weak. It is recommended to provide more literature
support for this decision in the discussion.
2. Data Analysis
The paper handles data meticulously, using R software for data cleaning, feature selection, and
model validation, which is in line with standard statistical methods. However, some important
metrics, such as sensitivity (0.40), are relatively low, which may affect the model's practical
prediction performance. It is advised to explore ways to enhance the sensitivity of the model
while retaining its high specificity (0.936).
3. Discussion and Conclusion
The discussion section covers most relevant points, noting the limitations of existing scoring
systems and highlighting the advantages of the new model. However, there is not enough
in-depth discussion on the clinical applicability and future research directions, particularly
concerning the model’s adaptability and limitations across different patient populations or
conditions. It is suggested to expand on these points, especially on potential challenges in
applying the model in real-world settings.
My recommendation to this article is accept after minor revision.
Please review this paper.
user
Title: Construction and validation of a predictive model for the risk of rebleeding in patients
with esophageal and gastric varices hemorrhage
Version: 3 Date: 04 Nov 2024
Reviewer's report:
This paper aims to construct and validate a predictive model for the risk of rebleeding in patients
with esophageal and gastric varices hemorrhage (EGVH) and compares it with existing scoring
systems such as GBS and pRS. Based on retrospective clinical data from 265 patients, the study
constructs a model using binary logistic regression and validates its predictive ability through
metrics like ROC curves and AUC. While the study holds clinical significance, several aspects
require improvement.
1. Methodology
Sample Size: This is a single-center retrospective study with a relatively small sample size (265
patients), which may impact the model’s external validity. It is recommended that future studies
include larger, multi-center prospective trials to further validate the model's stability and broader
applicability.
Factor Selection: The study excluded certain conventional factors, such as hemoglobin levels,
from the model and instead chose transfusion requirements as a predictive factor. While this is
justifiable, the rationale appears somewhat weak. It is recommended to provide more literature
support for this decision in the discussion.
2. Data Analysis
The paper handles data meticulously, using R software for data cleaning, feature selection, and
model validation, which is in line with standard statistical methods. However, some important
metrics, such as sensitivity (0.40), are relatively low, which may affect the model's practical
prediction performance. It is advised to explore ways to enhance the sensitivity of the model
while retaining its high specificity (0.936).
3. Discussion and Conclusion
The discussion section covers most relevant points, noting the limitations of existing scoring
systems and highlighting the advantages of the new model. However, there is not enough
in-depth discussion on the clinical applicability and future research directions, particularly
concerning the model’s adaptability and limitations across different patient populations or
conditions. It is suggested to expand on these points, especially on potential challenges in
applying the model in real-world settings.
My recommendation to this article is accept after minor revision.
assistant