Corrigendum to "Identifying treatment non-responders based on pre-treatment gait characteristics - A machine learning approach." [Volume , Issue 11, November 2023, e21242].

[This corrects the article DOI: 10.1016/j.heliyon.

M.I.N.I.-KID interviews with adolescents: a corpus-based language analysis of adolescents with depressive disorders and the possibilities of continuation using Chat GPT.

Background: Up to 13% of adolescents suffer from depressive disorders. Despite the high psychological burden, adolescents rarely decide to contact child and adolescent psychiatric services. To provide a low-barrier alternative, our long-term goal is to develop a chatbot for early identification of depressive symptoms.

Status of surface electromyography assessment as part of clinical gait analysis in the management of patients with cerebral palsy - Outcomes of a Delphi process.

Background: The assessment of gait disorders in patients with neuromotor conditions, such as cerebral palsy (CP), has been a focus of clinical and research attention, with electromyography (EMG) offering a nuanced understanding of neurological and neuromuscular disorders. However, the interpretation of EMG data in the context of gait analysis remains challenging due to the complexity of neuromotor dynamics and variability in assessment methodologies.

Research Question: To which consensus can we get in a group of experts in the fields of neurological and neuromuscular disorders, biomechanics, and clinical gait analysis to establish standardized protocols and a common language for the measurement and analysis of EMG data in gait disorders, particularly in people living with CP?

Methods: A three-round Delphi process was conducted from February to September 2023 to gather opinions of 53 experts on the use of surface EMG data during gait in the context of CP.

Pathways From Socioeconomic Factors to Major Cardiovascular Events Among Postmenopausal Veteran and Nonveteran Women: Findings From the Women's Health Initiative.

Background: Cardiovascular disease (CVD) remains a leading cause of death for women in the United States, with veterans being at potentially higher risk than their nonveteran counterparts due to accelerated aging and distinct biopsychosocial mechanisms. We examined pathways between selected indicators of socioeconomic status (SES) such as education, occupation, household income, and neighborhood SES and major CVD events through lifestyle and health characteristics among veteran and nonveteran postmenopausal women.

Methods And Results: A total of 121 286 study-eligible WHI (Women's Health Initiative) participants (3091 veterans and 118 195 nonveterans) were prospectively followed for an average of 17 years, during which 16 108 major CVD events were documented.