Review for 10.1186/s12876-025-03668-7 (2025)

Title: Machine Learning for Temporary Stoma After Intestinal Resection in Surgical Decision-
Making of Crohn's Disease
Version: 0 Date: 09 Jul 2024
Reviewer's report:
PEER REVIEWER ASSESSMENTS:
OBJECTIVE - Full research articles: is there a clear objective that addresses a testable research
question(s) (brief or other article types: is there a clear objective)?
Yes - there is a clear objective
DESIGN - Is the current approach (including controls and analysis protocols) appropriate for
the objective?
No - there are minor issues
EXECUTION - Are the experiments and analyses performed with technical rigor to allow
confidence in the results?
No - there are major issues
STATISTICS - Is the use of statistics in the manuscript appropriate?
No - there are issues with the statistics in the study
INTERPRETATION - Is the current interpretation/discussion of the results reasonable and not
overstated?
No - there are major issues
OVERALL MANUSCRIPT POTENTIAL - Is the current version of this work technically
sound? If not, can revisions be made to make the work technically sound?
No - manuscript has some fundamental flaw(s)
PEER REVIEWER COMMENTS:
GENERAL COMMENTS: Overall, the study presents a potentially valuable tool for surgical
decision-making in Crohn's disease. However, it has significant limitations in study design and
data analysis. For instance, the retrospective nature of the study makes it more susceptible to
biases such as selection bias, which can occur if the cohort does not accurately represent the
general population. Additionally, being conducted at a single center may limit the diversity of
the patient population, thereby restricting the generalizability of the findings to other settings
or regions. Furthermore, there are substantial issues in data analysis, results presentation, and
interpretation, which I detailed below.
REQUESTED REVISIONS:
The manuscript did not sufficiently detail the inclusion and exclusion criteria, nor did it
specify the number of patients excluded due to incomplete information. This omission could
compromise the interpretation of data robustness and the reliability of the study findings.
The exclusion of patients with incomplete data may not be the most effective approach to handle
missing information. It would be beneficial if the authors considered applying a method for
missing data imputation to address this issue more appropriately.
The description of the statistical analysis was too brief and vague. Essential details that are
critical for a prediction study, such as the definition of predictors and the outcome variable,
the process of feature scaling or standardization, and specifics of the prediction model (i.e., the
multivariate logistic regression model), were missing. The authors should consult the TRIPOD
statement for comprehensive guidance on reporting materials and methods in prediction studies.
Relying solely on univariate statistical models for variable selection is inadequate. Advanced
methods such as regularized regression or recursive feature elimination should be considered
to enhance model robustness.
It appears that no internal cross-validation was conducted during the model training. Cross-
validation is crucial to prevent issues like overfitting, particularly when data are limited, as it
maximizes the use of available data for training and validation.
Do the data presented in Tables 1 and 2 pertain only to the training cohort? This needs to be
clarified. Additionally, similar tables should be provided for the validation cohort to allow for
a comprehensive comparison.
Figure 1 may be considered for removal, as it appears to provide minimal information relevant
to the predictive analysis.
The conclusions may overstate the predictive capability of the nomogram, given the limitations
acknowledged by the authors. It is important to temper claims to align them more closely with
the level of evidence and validation provided by the study.