Title: Prediction and management of strangulated bowel obstruction: A multi-dimensional
model analysis
Version: 2 Date: 13 Mar 2022
Reviewer:  Enrico Mossotto
Reviewer's report:
Authors described a statistical model employing radiological and clinical variables to
discriminate patients presenting with strangulated bowel obstruction from those with a simple
bowel obstruction. Furthermore, they investigate the discriminatory effect of such features and
select the most relevant for constructing two models: one uniquely based on radiological features
and one including both radiological and clinical features.
While the manuscript clearly shows the advantage of using multiple types of features (the
"multiomics" model performs significantly better than the radiomics model), the following
points should be addressed and taken in consideration:
1. Authors refers to their approach as multiomics, however none of the ‘omics were actually
employed. The term radiomics is misused as none of the radiological features were extracted
using artificial intelligence/machine learning methods and raw images were not included in
the model. In this manuscript, authors use radiological reports as source of their radiological
features. Therefore, the use of the term ‘multiomics’ should be avoided throughout the
manuscript.
2. With regards to the predictive model, authors should disclose which approach they employed
to avoid overfitting the model (e.g. was cross validation applied?).
3. Overall, the statistical models should be described more in depth in the method section
indicating the parameters used.
4. Authors should indicate their approach to feature correlations and co-linearity as it might
highly bias the results from the multivariate regression.
5. p-values should also be corrected for multiple tests (e.g. Table 1 reports 24 tests) and authors
should carefully interpret unadjusted results.
6. Authors do not provide results with regards to the validation of the risk score on independent
(or set-aside) data.
Title: Prediction and management of strangulated bowel obstruction: A multi-dimensional
model analysis
Version: 2 Date: 13 Mar 2022
Reviewer:  Enrico Mossotto
Reviewer's report:
Authors described a statistical model employing radiological and clinical variables to
discriminate patients presenting with strangulated bowel obstruction from those with a simple
bowel obstruction. Furthermore, they investigate the discriminatory effect of such features and
select the most relevant for constructing two models: one uniquely based on radiological features
and one including both radiological and clinical features.
While the manuscript clearly shows the advantage of using multiple types of features (the
"multiomics" model performs significantly better than the radiomics model), the following
points should be addressed and taken in consideration:
1. Authors refers to their approach as multiomics, however none of the ‘omics were actually
employed. The term radiomics is misused as none of the radiological features were extracted
using artificial intelligence/machine learning methods and raw images were not included in
the model. In this manuscript, authors use radiological reports as source of their radiological
features. Therefore, the use of the term ‘multiomics’ should be avoided throughout the
manuscript.
2. With regards to the predictive model, authors should disclose which approach they employed
to avoid overfitting the model (e.g. was cross validation applied?).
3. Overall, the statistical models should be described more in depth in the method section
indicating the parameters used.
4. Authors should indicate their approach to feature correlations and co-linearity as it might
highly bias the results from the multivariate regression.
5. p-values should also be corrected for multiple tests (e.g. Table 1 reports 24 tests) and authors
should carefully interpret unadjusted results.
6. Authors do not provide results with regards to the validation of the risk score on independent
(or set-aside) data.
Please review this paper.
user
Title: Prediction and management of strangulated bowel obstruction: A multi-dimensional
model analysis
Version: 2 Date: 13 Mar 2022
Reviewer:  Enrico Mossotto
Reviewer's report:
Authors described a statistical model employing radiological and clinical variables to
discriminate patients presenting with strangulated bowel obstruction from those with a simple
bowel obstruction. Furthermore, they investigate the discriminatory effect of such features and
select the most relevant for constructing two models: one uniquely based on radiological features
and one including both radiological and clinical features.
While the manuscript clearly shows the advantage of using multiple types of features (the
"multiomics" model performs significantly better than the radiomics model), the following
points should be addressed and taken in consideration:
1. Authors refers to their approach as multiomics, however none of the ‘omics were actually
employed. The term radiomics is misused as none of the radiological features were extracted
using artificial intelligence/machine learning methods and raw images were not included in
the model. In this manuscript, authors use radiological reports as source of their radiological
features. Therefore, the use of the term ‘multiomics’ should be avoided throughout the
manuscript.
2. With regards to the predictive model, authors should disclose which approach they employed
to avoid overfitting the model (e.g. was cross validation applied?).
3. Overall, the statistical models should be described more in depth in the method section
indicating the parameters used.
4. Authors should indicate their approach to feature correlations and co-linearity as it might
highly bias the results from the multivariate regression.
5. p-values should also be corrected for multiple tests (e.g. Table 1 reports 24 tests) and authors
should carefully interpret unadjusted results.
6. Authors do not provide results with regards to the validation of the risk score on independent
(or set-aside) data.
assistant