2024 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces.

This is the eighth annual summary of the International Liaison Committee on Resuscitation International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations; a more comprehensive review was done in 2020. This latest summary addresses the most recent published resuscitation evidence reviewed by the International Liaison Committee on Resuscitation task force science experts. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, using Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations.

Recognition and Awareness of Sepsis by First-Aid Providers in Adults With Suspected Infection: A Scoping Review.

Sepsis accounts for a significant proportion of preventable deaths worldwide and early treatment has been found to be a mainstay of decreasing mortality. Early identification of sepsis in the first-aid setting is critical as this results in a shorter time to hospital presentation and management with antibiotics and initial resuscitation. Our aim was to explore the existing literature related to either sepsis recognition or awareness of sepsis by first-aid providers who are evaluating an adult suspected of an acute infection.

Inter-Rater and Intra-Rater Agreement in Scoring Severity of Rodent Cardiomyopathy and Relation to Artificial Intelligence-Based Scoring.

We previously developed a computer-assisted image analysis algorithm to detect and quantify the microscopic features of rodent progressive cardiomyopathy (PCM) in rat heart histologic sections and validated the results with a panel of five veterinary toxicologic pathologists using a multinomial logistic model. In this study, we assessed both the inter-rater and intra-rater agreement of the pathologists and compared pathologists' ratings to the artificial intelligence (AI)-predicted scores. Pathologists and the AI algorithm were presented with 500 slides of rodent heart.