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Authors’ Response to Peer Reviews of “Cardiotoxicity in Pediatric Cancer Survivorship: Retrospective Cohort Study”

Authors’ Response to Peer Reviews of “Cardiotoxicity in Pediatric Cancer Survivorship: Retrospective Cohort Study”

The study relies heavily on self-reported cardiovascular complications, which may introduce reporting bias. While a subset of cases was validated via medical records, the proportion of validated cases is not explicitly stated, and the possibility of underreporting or overreporting remains. The reliance on self-reported cardiovascular complications may have introduced reporting bias into the study.

Masab Mansoor, Andrew Ibrahim

JMIRx Med 2025;6:e79672

Peer Review of “Cardiotoxicity in Pediatric Cancer Survivorship: Retrospective Cohort Study”

Peer Review of “Cardiotoxicity in Pediatric Cancer Survivorship: Retrospective Cohort Study”

This significant and timely manuscript [1], which investigates the long-term cardiovascular complications in pediatric cancer survivors, has notable strengths, including its large cohort size, long-term follow-up, and utilization of a well-established dataset (Childhood Cancer Survivor Study). The methodology is generally sound, and the findings contribute meaningfully to our understanding of cardiotoxicity risks in childhood cancer survivors.

John Lucas Jr

JMIRx Med 2025;6:e79523

Cardiotoxicity in Pediatric Cancer Survivorship: Retrospective Cohort Study

Cardiotoxicity in Pediatric Cancer Survivorship: Retrospective Cohort Study

Among these late effects, cardiovascular complications have emerged as a leading cause of morbidity and mortality in childhood cancer survivors [2]. Cardiotoxicity, a term encompassing a spectrum of cardiovascular adverse effects, can manifest in various forms including cardiomyopathy, coronary artery disease, valvular heart disease, and arrhythmias [3].

Masab Mansoor, Andrew Ibrahim

JMIRx Med 2025;6:e65299

A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study

A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study

As a proof of concept, we apply our framework to predict cardiovascular disease (CVD) outcomes, myocardial infarction (MI), and stroke, among people with type 2 diabetes (T2 D). With CVD being a leading cause of death in the United States, and patients with T2 D being at elevated risk of CVD, it is urgent to develop accurate and fair predictive models that generate clinically reasonable predictions [16-19].

Yang Yang, Che-Yi Liao, Esmaeil Keyvanshokooh, Hui Shao, Mary Beth Weber, Francisco J Pasquel, Gian-Gabriel P Garcia

JMIR Med Inform 2025;13:e66200

Effectiveness of a WeChat Mini Program–Based Intervention on Promoting Multiple Health Behavior Changes Among Chinese Patients With Cardiovascular Diseases in Home-Based Rehabilitation: Randomized Controlled Trial

Effectiveness of a WeChat Mini Program–Based Intervention on Promoting Multiple Health Behavior Changes Among Chinese Patients With Cardiovascular Diseases in Home-Based Rehabilitation: Randomized Controlled Trial

Cardiovascular diseases (CVDs), the leading cause of mortality globally, encompass a variety of disorders that impair the heart and blood vessels (eg, coronary heart disease, cerebrovascular disease, and peripheral arterial disease) [1,2]. In China, approximately 330 million individuals are afflicted with CVDs, constituting 46.9% of total annual deaths [3].

Yanping Duan, Wei Liang, Lan Guo, Huimin Zhan, Chunli Xia, Huan Ma, Borui Shang, Yanping Wang, Min Yang, Shishi Cheng

J Med Internet Res 2025;27:e66249

Experience of Cardiovascular and Cerebrovascular Disease Surgery Patients: Sentiment Analysis Using the Korean Bidirectional Encoder Representations from Transformers (KoBERT) Model

Experience of Cardiovascular and Cerebrovascular Disease Surgery Patients: Sentiment Analysis Using the Korean Bidirectional Encoder Representations from Transformers (KoBERT) Model

Data pertaining to cerebrovascular and cardiovascular surgeries were visualized and analyzed using word clouds (Multimedia Appendix 2). For cerebrovascular surgery patients, the prominent keywords were “surgery,” “hospital,” “test,” “receive,” and “symptom.” For cardiovascular surgery patients, the key terms that emerged were “surgery,” “hospital,” “receive,” “eat,” “admission,” and “symptom.”

Hocheol Lee, Yu Seong Hwang, Ye Jun Kim, Yukyung Park, Heui Sug Jo

JMIR Med Inform 2025;13:e65127

Augmenting Engagement in Decentralized Clinical Trials for Atrial Fibrillation: Development and Implementation of a Programmatic Architecture

Augmenting Engagement in Decentralized Clinical Trials for Atrial Fibrillation: Development and Implementation of a Programmatic Architecture

Women, racial and ethnic minorities, and people who reside in rural settings have historically been underrepresented in randomized clinical trials testing or evaluating interventions for cardiovascular diseases [2-6]. Causes of such underrepresentation are multifactorial and related to the individual or patient, investigator, and health care system factors [1,7,8].

Toluwa Daniel Omole, Andrew Mrkva, Danielle Ferry, Erin Shepherd, Jessica Caratelli, Noah Davis, Richmond Akatue, Timothy Bickmore, Michael K Paasche-Orlow, Jared W Magnani

JMIR Cardio 2025;9:e66436