Review for 10.1186/s12876-024-03586-0 (2025)

Title: A validation Study of Three Early Warning Scores in Early Identification of Gastric
Cancer Patients with Deteriorating Condition after Gastrectomy
Version: 2 Date: 01 Sep 2024
Reviewer's report:
Dear authors,
I commend on the research idea for comparing the three EWS scoring systems in gastric cancer
patients with deteriorating condition after gastrectomy. I have the following comments:
1- The title needs to clearly state that this is a validation study of  three early warning
scores. following the “Transparent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis” (TRIPOD) statement (Karel G.M. Moons et al. 2015).
2- Clearly state the aim is to validate the three scoring systems in the abstract
3- Before presenting the results of the models’ discrimination and calibration, you need to
provide a table comparing the cases and control groups showing all the items of the three scoring
systems are calculated with a comparison in the final scores between the two groups. The full
details might be added as a supplementary table, but at least show the differences in final scores
in a table in the body of the article.
4- In table 3 narrative you mentioned: “The differences in the three scoring systems 72h before
the onset of deterioration were statistically significant between the case and control groups
(p<0.01)# as shown in Table 3.”, however, Table 3 does not show the comparisons between the
cases and control group regarding the three scoring systems.
5- Some sentences need to be included in the methods rather than the results section:
a. “Discrimination # the ability to distinguish patients who have experienced events from those
who have not# is commonly assessed using receiver operating characteristic (ROC) curves [21#
22]. “
b. It  is generally believed that an area of less than 0.60 indicates poor discrimination; 0.60-0.75
indicates good discrimination; and >0.75 indicates better discrimination [23-25]. This sentence
should be modified also and transferred to the methods: the original text in the  reference
you used was: “A generally accepted approach suggests that an area under the ROC curve
or C statistic of less than 0.60 reflects poor discrimination; 0.60 to 0.75, possibly helpful
discrimination; and more than 0.75, clearly useful Discrimination” , I suggest you may use
fair (or acceptable) and good instead of good and better or you can you the general guidelines
mentioned by Hosmer DW, Lemeshow S in Reference number 25.
6- Under the model calibration you mentioned: “Calibration ability is assessed using the
Hosmer-Lemeshow goodness-of-fit test[26 ]” the reference number should be 25 not 26.
7- Replace the words “accompanying  probabilities” by: “with p values”.
8- The quality of Figure 3 could be improved, instead of using a screenshot you may use R or
other software to combine the three calibration plots
9- The calibration curves (reliability diagrams) are better to be included to assess how the points
are close the best fit line. The Hosmer-Lemeshow test is sensitive with large sample sizes and
may lead to wrong conclusion in deciding which model has good calibration.
10-  Replace all > 0.05 everywhere by one statement in the statistical analysis in the methods
stating that the significance level was set at 0.05 in all analyses.
11-  No need to explain the utility of the decision curve analysis in the results, move this to the
statistical analysis in the methods.
12-  Add a reference for this statement: “SEWS can be used to identify gastric cancer resection
patients at risk of postoperative complications and is currently the most suitable early warning
scoring tool for observing gastric cancer resection patients in the early postoperative period”.
13-  The conclusion is too long, make it more concise in one paragraph without restating the
discrimination and calibration results again.