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Identification of Major Bleeding Events in Postoperative Patients With Malignant Tumors in Chinese Electronic Medical Records: Algorithm Development and Validation

Identification of Major Bleeding Events in Postoperative Patients With Malignant Tumors in Chinese Electronic Medical Records: Algorithm Development and Validation

To more accurately identify high-risk individuals for bleeding, we chose to focus on patients with chest, abdominal and gynecological malignant tumor instead of urinary system tumors. This decision was based on the fact that surgeries for chest, abdominal, and gynecological tumors involve greater surgical trauma, making postoperative bleeding more likely.

Hui Li, Haiyang Yao, Yuxiang Gao, Hang Luo, Changbin Cai, Zhou Zhou, Muhan Yuan, Wei Jiang

JMIR Form Res 2025;9:e66189

Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study

Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study

Waist circumference (WC) is a simple method to evaluate abdominal adiposity that is easy to standardize. It is also an independent cardiovascular risk factor, with a higher predicting value than BMI [2,3]. However, this measurement is not routinely used in clinical practice. Recently, a computed tomography (CT) scan estimation became a valid measure of standing WC [4,5]. This method is truly valuable in retrospective studies, where it can be difficult to obtain such measurements.

Abderrahmen Masmoudi, Amine Zouari, Ahmed Bouzid, Kais Fourati, Soulaimen Baklouti, Mohamed Ben Amar, Salah Boujelben

JMIRx Med 2023;4:e38852

Peer Review of “Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study”

Peer Review of “Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study”

What kind of procedure was used to perform the diagnostic test to detect abdominal obesity? Please discuss this in the Methods section. Thanks to the authors for providing a detailed revised version and comments. If the authors can clear up a few more confusions, then it would be great.  1. The authors stated that the app has an accuracy of 83% when using the m WC to detect abdominal obesity. Is it sufficient compared to the conventional approaches? Just a simple comparison/comment would suffice.  2.

Mohammed Shahriar Arefin

JMIRx Med 2023;4:e54012