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ENABLE—App-Based Digital Capture and Intervention of Patient-Reported Quality of Life, Adverse Events, and Treatment Satisfaction in Breast Cancer: Protocol for a Randomized Controlled Trial

ENABLE—App-Based Digital Capture and Intervention of Patient-Reported Quality of Life, Adverse Events, and Treatment Satisfaction in Breast Cancer: Protocol for a Randomized Controlled Trial

The study was approved by the central ethics committee in Heidelberg (S-658/2020). All recruiting centers obtained approval from the local ethics committees (Ethics Committee at the Medical Faculty of the Eberhard Karls University of Tuebingen and the University Hospital of Tuebingen: 896/2020 BO2; Ethics Committee II of the University of Heidelberg, Mannheim Medical Faculty: 2021-544) before study initiation. Written informed consent was obtained from all participants before study participation.

Thomas M Deutsch, Léa L Volmer, Manuel Feisst, Laura Bodenbeck, Kathrin Hassdenteufel, Lara Tretschock, Christiane Breit, Stefan Stefanovic, Armin Bauer, Carolin Anders, Lina Weinert, Tobias Engler, Andreas D Hartkopf, Nico Pfeifer, Pascal Escher, Marc Mausch, Oliver Heinze, Marc Suetterlin, Sara Y Brucker, Andreas Schneeweiss, Markus Wallwiener

JMIR Res Protoc 2025;14:e69855

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

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