Research priorities for elder abuse screening and intervention: A Geriatric Emergency Care Applied Research (GEAR) network scoping review and consensus statement.

The Geriatric Emergency Care Applied Research (GEAR) Network (1) conducted a scoping review of the current literature on the identification of and interventions to address elder abuse among patients receiving care in emergency departments and (2) used this review to prioritize research questions for knowledge development. Two questions guided the scoping review: What is the effect of universal emergency department screening compared to targeted screening or usual practice on cases of elder abuse identified, safety outcomes, and health care utilization?; and What is the safety, health, legal, and psychosocial impact of emergency department-based interventions vs. usual care for patients experiencing elder abuse? We searched five article databases.

White Paper-Geriatric Emergency Medicine Education: Current State, Challenges, and Recommendations to Enhance the Emergency Care of Older Adults.

Older adults account for 25% of all emergency department (ED) patient encounters. One in five Americans will be 65 or older by 2030. In response to this need, geriatric emergency medicine (GEM) has developed into a robust area of academic and clinical interest, with extensive evidence-based research and guidelines, including clear undergraduate and postgraduate GEM competencies.

Non-invasive determination of surface features of banana during ripening.

Ripening of dessert banana () is associated with changes in colour (green to yellow starting from the cente), softening, and surface features. These have mostly been investigated using distinct technologies. Hence, here changes in surface features were examined with two novel, non-invasive techniques: a luster sensor and a 3D profilometer.

Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model.

Background: Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive, fatal motor neuron disease with a variable natural history. There are no accurate models that predict the disease course and outcomes, which complicates risk assessment and counselling for individual patients, stratification of patients for trials, and timing of interventions. We therefore aimed to develop and validate a model for predicting a composite survival endpoint for individual patients with ALS.