Title: Preoperative Optimization Strategy Management Model Applied in Gallbladder Surgery
Version: 2 Date: 09 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The background addresses challenges in developing countries, such as doctor-patient
communication, process management, and informatization. However, these challenges are not
fully elaborated upon. It would be beneficial to expand on these issues and explain how the
model addresses them, particularly in healthcare settings that may lack the same resources or
infrastructure found in developed countries.
-While the pre-admission management model is introduced, the specific components and
processes involved in the "optimized" model are not thoroughly explained. For instance, how are
virtual beds managed, and what specific tools (such as IT systems and communication platforms)
are used to coordinate this process? Providing further details on how the virtual and physical
components interact would help readers comprehend the practical aspects of implementation.
-Although the inclusion and exclusion criteria are presented, additional information on the
cluster sampling methodology would be helpful. For example, how were the two patient groups
(ordinary inpatients and pre-inpatients) selected? Were they matched based on key demographic
and clinical factors such as age, comorbidities, and type of surgery? Matching is essential to
ensure that any observed differences in outcomes are due to the pre-admission process rather
than confounding variables.
-While the statistical methods utilized are appropriate, it would be useful to mention how
confounding variables were controlled for in the analysis. For example, if there were significant
age differences between the groups, did the analysis account for this when comparing outcomes?
A more detailed explanation of the methods used to control for potential confounders would
strengthen the study’s conclusions.
-Although age and underlying conditions are cited as factors affecting hospitalization outcomes,
the study does not provide an in-depth analysis of other potential confounders, such as
socioeconomic status, hospital-specific factors (e.g., bed capacity), or surgical complications. A
more robust multivariate analysis that includes these factors could yield a more comprehensive
understanding of the impact of the pre-admission management model.
-What specific strategies were implemented to support elderly patients in this process, and how
might these strategies be optimized? Moreover, what specific reasons led to the decision of
26 patients to opt out, and how can the model be improved to accommodate those who face
challenges with technology or communication?
Title: Preoperative Optimization Strategy Management Model Applied in Gallbladder Surgery
Version: 2 Date: 09 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The background addresses challenges in developing countries, such as doctor-patient
communication, process management, and informatization. However, these challenges are not
fully elaborated upon. It would be beneficial to expand on these issues and explain how the
model addresses them, particularly in healthcare settings that may lack the same resources or
infrastructure found in developed countries.
-While the pre-admission management model is introduced, the specific components and
processes involved in the "optimized" model are not thoroughly explained. For instance, how are
virtual beds managed, and what specific tools (such as IT systems and communication platforms)
are used to coordinate this process? Providing further details on how the virtual and physical
components interact would help readers comprehend the practical aspects of implementation.
-Although the inclusion and exclusion criteria are presented, additional information on the
cluster sampling methodology would be helpful. For example, how were the two patient groups
(ordinary inpatients and pre-inpatients) selected? Were they matched based on key demographic
and clinical factors such as age, comorbidities, and type of surgery? Matching is essential to
ensure that any observed differences in outcomes are due to the pre-admission process rather
than confounding variables.
-While the statistical methods utilized are appropriate, it would be useful to mention how
confounding variables were controlled for in the analysis. For example, if there were significant
age differences between the groups, did the analysis account for this when comparing outcomes?
A more detailed explanation of the methods used to control for potential confounders would
strengthen the study’s conclusions.
-Although age and underlying conditions are cited as factors affecting hospitalization outcomes,
the study does not provide an in-depth analysis of other potential confounders, such as
socioeconomic status, hospital-specific factors (e.g., bed capacity), or surgical complications. A
more robust multivariate analysis that includes these factors could yield a more comprehensive
understanding of the impact of the pre-admission management model.
-What specific strategies were implemented to support elderly patients in this process, and how
might these strategies be optimized? Moreover, what specific reasons led to the decision of
26 patients to opt out, and how can the model be improved to accommodate those who face
challenges with technology or communication?
Please review this paper.
user
Title: Preoperative Optimization Strategy Management Model Applied in Gallbladder Surgery
Version: 2 Date: 09 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The background addresses challenges in developing countries, such as doctor-patient
communication, process management, and informatization. However, these challenges are not
fully elaborated upon. It would be beneficial to expand on these issues and explain how the
model addresses them, particularly in healthcare settings that may lack the same resources or
infrastructure found in developed countries.
-While the pre-admission management model is introduced, the specific components and
processes involved in the "optimized" model are not thoroughly explained. For instance, how are
virtual beds managed, and what specific tools (such as IT systems and communication platforms)
are used to coordinate this process? Providing further details on how the virtual and physical
components interact would help readers comprehend the practical aspects of implementation.
-Although the inclusion and exclusion criteria are presented, additional information on the
cluster sampling methodology would be helpful. For example, how were the two patient groups
(ordinary inpatients and pre-inpatients) selected? Were they matched based on key demographic
and clinical factors such as age, comorbidities, and type of surgery? Matching is essential to
ensure that any observed differences in outcomes are due to the pre-admission process rather
than confounding variables.
-While the statistical methods utilized are appropriate, it would be useful to mention how
confounding variables were controlled for in the analysis. For example, if there were significant
age differences between the groups, did the analysis account for this when comparing outcomes?
A more detailed explanation of the methods used to control for potential confounders would
strengthen the study’s conclusions.
-Although age and underlying conditions are cited as factors affecting hospitalization outcomes,
the study does not provide an in-depth analysis of other potential confounders, such as
socioeconomic status, hospital-specific factors (e.g., bed capacity), or surgical complications. A
more robust multivariate analysis that includes these factors could yield a more comprehensive
understanding of the impact of the pre-admission management model.
-What specific strategies were implemented to support elderly patients in this process, and how
might these strategies be optimized? Moreover, what specific reasons led to the decision of
26 patients to opt out, and how can the model be improved to accommodate those who face
challenges with technology or communication?
assistant