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Revisiting the Semantic Severity of Anxiety and Depression: Computational Linguistic Study of Normalization and Pathologization

Revisiting the Semantic Severity of Anxiety and Depression: Computational Linguistic Study of Normalization and Pathologization

First, we trained a Continuous Bag of Words word2vec model on the entire corpus using the word2vec package in R (R Foundation for Statistical Computing) [27]. Next, we created a mental health vector by averaging the vectors of words strongly associated with mental health (eg, therapy, psychiatry, and diagnosis; see Multimedia Appendix 1 for details). This vector was used as a reference point for identifying mental health–related language.

Vojtech Pisl, Ana-Maria Bucur, Ioana R Podina

J Med Internet Res 2025;27:e73950

Digital Infrared Thermographic Imaging for Limb Salvage in Patients at Risk of Amputation: Prospective Observational Study

Digital Infrared Thermographic Imaging for Limb Salvage in Patients at Risk of Amputation: Prospective Observational Study

Statistical analysis was performed using the R software (version 4.1.2; R Foundation for Statistical Computing, including the R Commander package (version 2.9‐5). The Shapiro-Wilk test was used to verify the normality of the data distribution, and the nonparametric Friedman test to assess differences across the time points within each group.

Víctor Manuel Loza-González, Eleazar Samuel Kolosovas-Machuca, Patricia Aurea Cervantes-Báez, José Luis Ramírez-GarcíaLuna, Edgar Guevara, Mario Aurelio Martínez-Jiménez

JMIR Form Res 2025;9:e69072