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Exploring the Acceptance and Opportunities of Using a Specific Generative AI Chatbot to Assist Parents in Managing Pediatric Rheumatological Chronic Health Conditions: Mixed Methods Study

Exploring the Acceptance and Opportunities of Using a Specific Generative AI Chatbot to Assist Parents in Managing Pediatric Rheumatological Chronic Health Conditions: Mixed Methods Study

In total, there were 8 groups of mixed participants which included children and young people (n=9; age range 5‐26 y; mean age 13.3, SD 6.3 y) and parents (n=13). Each group consisted of 2‐5 participants. Participants felt that the chatbot needs to evolve with them. For instance, families felt that simple language should be used at the beginning of diagnosis. However, explanations should become more advanced in the subsequent years as families become more familiar and do not want to feel patronized.

Cheryl W Y Lau, Klaudia Kupiec, Polly Livermore

JMIR Pediatr Parent 2025;8:e70409

Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study

Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study

By calculating propensity scores based on race, ethnicity, sex, and birth year (within 2.5 y), we used a narrow caliper of 0.05 with a nearest neighbor approach to achieve a 1:5 case-to-control ratio for each prediction window group [21]. This rigorous methodology ensures a balanced study population for reliable analysis and EOCRC prediction.

Chengkun Sun, Erin Mobley, Michael Quillen, Max Parker, Meghan Daly, Rui Wang, Isabela Visintin, Ziad Awad, Jennifer Fishe, Alexander Parker, Thomas George, Jiang Bian, Jie Xu

JMIR Cancer 2025;11:e64506

Remote Photoplethysmography Technology for Blood Pressure and Hemoglobin Level Assessment in the Preoperative Assessment Setting: Algorithm Development Study

Remote Photoplethysmography Technology for Blood Pressure and Hemoglobin Level Assessment in the Preoperative Assessment Setting: Algorithm Development Study

A total of 200 patients with a mean age of 56.44 years (range: 21‐84 y) participated. Of these patients, 67% had comorbid conditions such as hypertension, diabetes mellitus, and ischemic heart disease. The demographic profile of the participants is given in Table 1. Demographic profile of patients (N=200). a SBP: systolic blood pressure. b DBP: diastolic blood pressure. We defined hypertension according to the European Society of Hypertension (ESH) guidelines [25].

Selene Y L Tan, Jia Xin Chai, Minwoo Choi, Umair Javaid, Brenda Pei Yi Tan, Belinda Si Ying Chow, Hairil Rizal Abdullah

JMIR Form Res 2025;9:e60455