Phenobarbital Versus Benzodiazepines for the Treatment of Severe Alcohol Withdrawal.

Background: Phenobarbital may offer advantages over benzodiazepines for severe alcohol withdrawal syndrome (SAWS), but its impact on clinical outcomes has not been fully elucidated.

Objective: The purpose of this study was to determine the clinical impact of phenobarbital versus benzodiazepines for SAWS.

Methods: This retrospective cohort study compared phenobarbital to benzodiazepines for the management of SAWS for patients admitted to progressive or intensive care units (ICUs) between July 2018 and July 2022.

Mental Practice: Applying Successful Strategies in Sports to the Practice of Emergency Medicine.

Emergency physicians are expected to learn and maintain a large and varied set of competencies for clinical practice. These include high acuity, low occurrence procedures that may not be encountered frequently in the clinical environment and are difficult to practice with high fidelity and frequency in a simulated environment. Mental practice is a form of a cognitive walk-through that has been shown to be an effective method for improving motor and cognitive skills, with literature in sports science and emerging evidence supporting its use in medicine.

The Value of Using a Quality Assurance Follow-Up Team to Address Incidental Findings After Emergency Department or Urgent Care Discharge: A Cost Analysis.

Background: Incidental finding (IF) follow-up is of critical importance for patient safety and is a source of malpractice risk. Laboratory, imaging, or other types of IFs are often uncovered incidentally and are missed, not addressed, or only result after hospital discharge. Despite a growing IF notification literature, a need remains to study cost-effective non-electronic health record (EHR)-specific solutions that can be used across different types of IFs and EHRs.

Predicting Response to Bolus Insulin Therapy in Patients With Type 2 Diabetes.

Background: The aim of this study was to develop a predictive model to classify people with type 2 diabetes (T2D) into expected levels of success upon bolus insulin initiation.

Methods: Machine learning methods were applied to a large nationally representative insurance claims database from the United States (dNHI database; data from 2007 to 2017). We trained boosted decision tree ensembles (XGBoost) to assign people into Class 0 (never meeting HbA1c goal), Class 1 (meeting but not maintaining HbA1c goal), or Class 2 (meeting and maintaining HbA1c goal) based on the demographic and clinical data available prior to initiating bolus insulin.