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Types of HPV Vaccine Misinformation Circulating on Twitter (X) That Parents Find Most Concerning: Insights From a Cross-Sectional Survey and Content Analysis

Types of HPV Vaccine Misinformation Circulating on Twitter (X) That Parents Find Most Concerning: Insights From a Cross-Sectional Survey and Content Analysis

Demographic information from a cross-sectional Internet survey of 263 US caregivers. The stimuli in this study consisted of 126 images of real tweets that contained misinformation about the HPV vaccine. These tweets were part of a larger collection from our previous study, a retrospective content analysis conducted from December 2019 to April 2020 [26]. All English language tweets associated with #HPV posted during this time period were included in the study.

Jennifer C Morgan, Sarah Badlis, Katharine J Head, Gregory Zimet, Joseph N Cappella, Melanie L Kornides

J Med Internet Res 2025;27:e54657

Area-Level Indices and Health Care Use in a Pediatric Brain and Central Nervous System Tumor Cohort: Observational Study

Area-Level Indices and Health Care Use in a Pediatric Brain and Central Nervous System Tumor Cohort: Observational Study

This cross-sectional analysis uses patient-level data from the Indiana Network for Patient Care research database, which includes most health care organizations throughout Indiana. We used data from this repository to identify pediatric patients with brain and CNS tumors.

Yvette H Tran, Seho Park, Scott L Coven, Eneida A Mendonca

JMIR Public Health Surveill 2025;11:e66834

Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and Medical Record Data: Machine Learning Approach

Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and Medical Record Data: Machine Learning Approach

Each model was trained using the 70% (42/60) subset and a cross-validation training control. A 10-fold cross-validation with 25% (15/60) left out replicated 3 times was used on each model to avoid bias and overfitting. From this, training accuracies are reported. Additional testing was performed for the prediction accuracy of each model using the 30% (18/60) test data.

Mihyun Lim Waugh, Tyler Mills, Nicholas Boltin, Lauren Wolf, Patti Parker, Ronnie Horner, Thomas L Wheeler II, Richard L Goodwin, Melissa A Moss

JMIR Form Res 2025;9:e59631

Improving Diet Quality of People Living With Obesity by Building Effective Dietetic Service Delivery Using Technology in a Primary Health Care Setting: Protocol for a Randomized Controlled Trial

Improving Diet Quality of People Living With Obesity by Building Effective Dietetic Service Delivery Using Technology in a Primary Health Care Setting: Protocol for a Randomized Controlled Trial

these five food groups with more variety (especially vegetables and fruit) every day Vegetables and legumes/beans, including different types and colors Fruit—choose fresh fruit as much as possible Grains (cereal) foods, mostly wholegrain and high cereal fiber varieties Lean meats and poultry, fish, eggs, tofu, nuts, and seeds, and legumes/beans (eg, kidney beans, chickpeas, lentils, and other beans) Milk, cheese, yoghurt, and alternatives, mostly reduced fat Drink more plain water (no added flavors); 2.1-2.6 L/

Deborah A Kerr, Clare E Collins, Andrea Begley, Barbara Mullan, Satvinder S Dhaliwal, Claire E Pulker, Fengqing Zhu, Marie Fialkowski, Richard L Prince, Richard Norman, Anthony P James, Paul Aveyard, Helen Mitchell, Jacquie Garton-Smith, Megan E Rollo, Chloe Maxwell-Smith, Amira Hassan, Hayley Breare, Lucy M Butcher, Christina M Pollard

JMIR Res Protoc 2025;14:e64735