Title: Predictive Modeling of Postoperative Gastrointestinal Dysfunction: The Role of Serum
Bilirubin, Sodium Levels, and Surgical Duration in Gynecological Cancer Care
Version: 7 Date: 27 Aug 2023
Reviewer's report:
Dear Authors,
Greetings,
I have carefully reviewed your manuscript titled "[Unveiling Key Predictors of Postoperative
Gastrointestinal Dysfunction in Gynecological Cancer: Insights for Personalized Management
Strategies]" and found that it contains valuable insights into predictive factors for postoperative
gastrointestinal dysfunction (POGD) in gynecological cancer patients. Your work has the
potential to contribute significantly to the field of gynecological oncology. However, I have
identified several areas where improvements could enhance the clarity, robustness, and impact
of your study. Please find below a comprehensive list of recommendations:
Abstract:
1. Specific Results: While your abstract briefly mentions identified predictive factors for POGD,
consider providing quantitative information such as odds ratios, effect sizes, or other relevant
metrics to convey the magnitude of these associations.
2. Keywords: To improve discoverability, add specific keywords related to gynecological cancer
types (e.g., "endometrial cancer," "cervical cancer," "ovarian cancer") and surgical procedures
(e.g., "laparoscopic surgery," "hysterectomy").
Introduction:
1. Citations: Introduce specific citations for epidemiological data regarding POGD in
gynecological cancer patients to bolster the credibility of your presented information.
2. Biochemical Parameters: Provide explanations for why serum bilirubin and sodium levels
might influence gastrointestinal functionality, helping readers understand the underlying
mechanisms.
3. Surgical Time and Anesthesia: Expand on how prolonged surgical time and specific anesthesia
types might impact gastrointestinal motility to make this connection clearer.
4. Predictive Model: Elaborate on the potential benefits of a predictive model, emphasizing how
it could facilitate risk assessment and targeted interventions.
Materials and Methods:
1. Inclusion and Exclusion Criteria: Offer a rationale for your inclusion and exclusion criteria
to provide context for readers, helping them understand why certain conditions were chosen for
exclusion.
2. Patient Monitoring for POGD: Explain the methodology used to monitor patients for POGD
and how POGD presence/absence was determined. Detail standardized criteria for assessing
POGD symptoms.
3. Outcome Measures: Clarify the specific symptoms or criteria used to classify patients as
experiencing POGD for better transparency.
4. Statistical Analysis: For each statistical test, explain the rationale behind its choice to provide
context for readers unfamiliar with these methods.
5. Multicollinearity Handling: If multicollinearity was detected, elaborate on how you addressed
this issue – whether through variable removal or other methods.
6. Missing Data: Acknowledge the limitations of listwise deletion for handling missing data,
addressing potential biases this approach might introduce.
7. SPSS Version: Explain why you chose SPSS version 25.0 for your analysis and consider
discussing any compatibility considerations.
8. Sensitivity Analyses: Provide a concise summary of the results of your sensitivity analyses to
shed light on how missing data might have affected your results.
Results :
1. Table Organization and Interpretation: While the tables offer a comprehensive view of the
data, consider reorganizing the presentation for clarity. Group related characteristics together
and provide concise explanations for any abbreviations or terminology used. This will facilitate
easier interpretation for readers.
2. Clinical Relevance Emphasis: Alongside statistical comparisons, ensure that you emphasize
the clinical significance and practical implications of observed differences. Clearly explain how
the identified factors could impact patient care, treatment decisions, and outcomes.
3. Effect Size Presentation: To enhance readers' understanding, include effect sizes (e.g., Cohen's
d, odds ratios) alongside p-values. This addition will allow readers to gauge the magnitude and
practical relevance of the observed differences.
4. Multivariate Analysis Explanation: In Table 3, provide a clear explanation of the transition
from univariate to multivariate analysis. Elaborate on why specific variables were included in
the multivariate model and how controlling for confounding factors affected the associations.
5. Limitations Discussion: While your results are informative, it's essential to discuss potential
limitations stemming from the study design or methodology. Addressing these limitations will
provide readers with a balanced perspective on the findings' scope and potential biases.
6. Graph Interpretation: If Figures 1, 2, and 3 are referenced in the text, ensure that their
content is thoroughly discussed. Highlight the key insights these figures provide and explain
their relevance to the study's objectives.
7. Nomogram Explanation: For Figure 3, which presents a nomogram for predicting POGD,
include a clear explanation in the text. Detail how readers should interpret the nomogram and
its practical utility.
8. Language Clarity: While the Results section language is generally clear, consider rephrasing
complex statistical concepts in more accessible terms to ensure all readers can grasp the content.
9. Comparative Analysis Context: In the case-control comparison (Table 2), provide additional
context about why you selected specific variables for comparison. Explain how the findings
contribute to the understanding of the patient population under study.
10. Concise Summary: Conclude the Results section with a concise paragraph summarizing the
main findings and their implications. This summary will help readers synthesize the presented
information.
Statistical Analyses Section:
1. Confounding Variables: Provide a comprehensive discussion of potential confounding
variables that might influence the observed associations. Identifying and addressing these
variables in your analysis, or at least acknowledging their presence, will enhance the validity
of your results.
2. Multicollinearity Assessment: Assess and report on the presence of multicollinearity among
predictor variables in your multivariate analysis. This is crucial to ensure the stability of the
model and the reliability of estimated coefficients.
3. Interaction Effects: Explore the presence of interaction effects between predictor variables,
particularly in the context of multivariate analysis. This exploration will shed light on whether
certain associations are influenced by simultaneous factors.
4. Model Fit Evaluation: Provide relevant goodness-of-fit statistics such as AIC (Akaike
Information Criterion) or BIC (Bayesian Information Criterion) to assess the appropriateness
and fit of your multivariate model to the data.
5. P-Value Adjustments: If multiple comparisons were conducted, consider adjusting p-values
for multiple testing using appropriate methods (e.g., Bonferroni correction) to mitigate the risk
of Type I errors.
6. Outcome Measures Clarity: Ensure that the outcome measures used in your analysis are clearly
defined and aligned with the study's objectives. Clarity in outcome definitions is essential to
prevent misinterpretation.
7. Subgroup Analysis Clarification: If you conducted subgroup analyses, ensure that you
introduce and justify them within the research context. Explain the rationale behind exploring
subgroups and their potential implications.
8. Effect Size Interpretation: Alongside p-values, emphasize the importance of effect sizes and
their clinical significance. Your interpretation should encompass both statistical and practical
relevance.
9. Sample Size Consideration: Discuss whether a power analysis was performed to determine
the required sample size. Including a statement about the study's power to detect significant
effects will enhance the credibility of your results.
10. Assumption Checking: Clearly state how you assessed assumptions such as normality,
homoscedasticity, and independence for each statistical test used in your analysis.
Discussion:
1.Comparative Insights and Contextualization: Offer more detailed insights into studies
reporting similar findings for better comparison, including methodologies and key results.
2. Role of Surgical Approach: Elaborate on the mechanisms through which less invasive surgical
approaches might influence POGD risk and address any conflicting findings in the literature.
3. Discussion on Outliers and Sensitivity Analysis: Discuss the outcomes of your outlier
identification and sensitivity analyses to inform readers about potential impacts on your
conclusions.
4.Discussion on Limitations and Further Recommendations: Provide a more explicit discussion
of limitations and their potential impact on generalizability and reliability. Suggest specific
directions for future research.
5. Applying Insights in Clinical Settings: Detail practical implementations of your findings in
clinical decision-making, explaining potential interventions or strategies based on your results.
6. Conclusion and Declarations: Revise your concluding remarks to ensure they accurately
reflect the study's contributions and implications, avoiding any template-specific content.
Conclusion:
1. Practical Application: Expand on how your insights could be practically applied in clinical
practice, offering tangible examples of how clinicians might use these findings.
2. Future Directions: Specify concrete directions for future research that build upon your
findings, focusing on patient subgroups or scenarios warranting further investigation.
3. Overall Impact: Highlight the broader impact of your study on advancing the field of
gynecological cancer care beyond the specific conditions studied.
4. Ethical Considerations and Declarations: Ensure that your concluding remarks focus on your
study's contributions and implications, excluding any template-related content.
References:
Ensure consistent formatting of references.
Verify completeness and consistency of citations.
Include DOI information for each reference if possible.
Use correct journal title abbreviations.
Remove references not cited in the manuscript text.
Best regards,