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Public Health Messaging About Dengue on Facebook in Singapore During the COVID-19 Pandemic: Content Analysis

Public Health Messaging About Dengue on Facebook in Singapore During the COVID-19 Pandemic: Content Analysis

Using the keywords “Dengue,” “dengue,” “Mozzie,” “mozzie,” “mosquito,” “breed,” “wolbachia,” “Wolbachia,” “aedes,” “BLOCK,” “B-L-O-C-K,” “SAW,” “S-A-W,” “#mozziewipeout,” “#Mozziewipeout,” “repellent,” “insecticide,” “vase,” “pails,” “pot,” and “soil,” we crawled Facebook posts of the 5 Singapore governmental institutions. We used the Python Web Crawler to crawl the Facebook posts, starting from January 1, 2020.

Shirley S Ho, Mengxue Ou, Nova Mengxia Huang, Agnes SF Chuah, Vanessa S Ho, Sonny Rosenthal, Hye Kyung Kim

JMIR Form Res 2025;9:e66954

Evaluating the Accuracy and Reliability of Real-World Digital Mobility Outcomes in Older Adults After Hip Fracture: Cross-Sectional Observational Study

Evaluating the Accuracy and Reliability of Real-World Digital Mobility Outcomes in Older Adults After Hip Fracture: Cross-Sectional Observational Study

For each WB, 6 gait characteristics were obtained from the single wearable device and the reference system: cadence (steps per minute; the number of steps taken per minute), stride length (meters; the length of 2 consecutive steps), number of steps, stride duration (seconds) [27], walking speed (m/s) [26], and distance (meters). The walked distance was calculated by multiplying 2 validated DMOs [27]: average walking speed × WB duration.

Martin A Berge, Anisoara Paraschiv-Ionescu, Cameron Kirk, Arne Küderle, Encarna Micó-Amigo, Clemens Becker, Andrea Cereatti, Silvia Del Din, Monika Engdal, Judith Garcia-Aymerich, Karoline B Grønvik, Clint Hansen, Jeffrey M Hausdorff, Jorunn L Helbostad, Carl-Philipp Jansen, Lars Gunnar Johnsen, Jochen Klenk, Sarah Koch, Walter Maetzler, Dimitrios Megaritis, Arne Müller, Lynn Rochester, Lars Schwickert, Kristin Taraldsen, Beatrix Vereijken

JMIR Form Res 2025;9:e67792

Benchmarking the Confidence of Large Language Models in Answering Clinical Questions: Cross-Sectional Evaluation Study

Benchmarking the Confidence of Large Language Models in Answering Clinical Questions: Cross-Sectional Evaluation Study

Our study corroborates GPT-4’s strong performance, particularly in psychiatry, where GPT-4o achieved 84.4% accuracy. However, our findings suggest that more cautious interpretation is needed, given the high confidence levels observed for incorrect answers. Xiong et al’s [17] work on LLM confidence elicitation aligns with our observations of overconfidence.

Mahmud Omar, Reem Agbareia, Benjamin S Glicksberg, Girish N Nadkarni, Eyal Klang

JMIR Med Inform 2025;13:e66917