Prepared, Protected, EmPowered (P3): Primary Results of a Randomized Controlled Trial Using a Social Networking, Gamification, and Coaching App to Promote Pre-exposure Prophylaxis (PrEP) Adherence for Sexual and Gender Minority (SGM) Youth Living in the United States.

The population-level reductions in HIV incidence attributed to pre-exposure prophylaxis (PrEP) have not been fully realized among sexual, gender, and racial/ethnic minority youth. P3 (Prepared, Protected, emPowered) is a comprehensive mobile application (app) intervention developed to support PrEP adherence through gamification, medication tracking, and social engagement. A randomized controlled trial was conducted with 246 young men who have sex with men (YMSM) and young transgender women who have sex with men (YTWMSM) currently on or planning to begin PrEP.

Technology-Based Interventions, with a Stepped Care Approach, for Reducing Sexual Risk Behaviors and Increasing PrEP Initiation Among Transgender and Gender Expansive Youth and Young Adults.

TechStep was a technology-based trial, with a stepped care approach, to reduce sexual risks and increase PrEP uptake among transgender and gender expansive youth and young adults (15-24 years old). From October 2019 to September 2021, 254 participants were randomized into: 1) Text (n = 82), or 2) Webapp (n = 87), or 3) Control (n = 85). At the 3-month follow-up assessment, those randomized to Text and Webapp and did not demonstrate improvement on primary outcomes were re-randomized to receive virtual eCoaching (Text + or Webapp +), or to remain in their initial condition without eCoaching.

Don't Let Your Analysis Go to Seed: On the Impact of Random Seed on Machine Learning-based Causal Inference.

Machine learning techniques for causal effect estimation can enhance the reliability of epidemiologic analyses, reducing their dependence on correct model specifications. However, the stochastic nature of many machine learning algorithms implies that the results derived from such approaches may be influenced by the random seed that is set before model fitting. In this work, we highlight the substantial influence of random seeds on a popular approach for machine learning-based causal effect estimation, namely doubly robust estimators.

Fungal Infection in Acutely Decompensated Cirrhosis Patients: Value of Model for End-Stage Liver Disease Score.

Background: Infection in acute-on-chronic liver failure (ACLF) patients is known to cause higher mortality. The current approach is to culture all patient samples. There are no published data evaluating fungal infections in acutely decompensated patients.

Practices and perceptions of cognitive assessment for adults with age-related hearing loss.

Objectives: To investigate the landscape of cognitive impairment (CI) screening for adults with age-related hearing loss (ARHL) among otolaryngologists and audiologists. To identify provider factors and patient characteristics that impact rates of CI screening and referral.

Methods: A 15 question online survey was sent to members of the Georgia Society of Otolaryngology (GSO), Georgia Academy of Audiology (GAA), American Otological Society and American Neurotology Society (AOS/ANS), and posted on the web forum for two hearing disorders special interest groups within the American-Speech-Language-Hearing Association (ASHA).

Safety and efficacy of antibiotics among acutely decompensated cirrhosis patients.

Background And Aim: Infection is a leading precipitant of acute-on-chronic liver failure. This study aims to determine the safety and efficacy of antibiotics within acute-on-chronic liver failure.

Methods: Retrospective study of 457 acute-on-chronic liver failure patients admitted to the University of Arizona Health Network between January 1 and December 31, 2014.