Who feels safe calling 911: are prior experiences of anti-Black racial discrimination associated with hesitancy seeking emergency medical services in the event of accidental drug overdose? - a study protocol.

Background: Racial discrimination is associated with health disparities among Black Americans, a group that has experienced an increase in rates of fatal drug overdose. Prior research has found that racial discrimination in the medical setting may be a barrier to addiction treatment. Nevertheless, it is unknown how experiences of racial discrimination might impact engagement with emergency medical services for accidental drug overdose.

Multicenter Analysis of the Relationship Between Operative Team Familiarity and Safety and Efficiency Outcomes in Cardiac Surgery.

Background: Safety in cardiac surgical procedures is predicated on effective team dynamics. This study associated operative team familiarity (ie, the extent of clinical collaboration among surgical team members) with procedural efficiency and Society of Thoracic Surgeons (STS) adjudicated patient outcomes.

Methods: Institutional STS adult cardiac surgery registry and electronic health record data from 2014 to 2021 were evaluated across 3 quaternary hospitals.

Neuropeptides specify and reprogram division of labor in the leafcutter ant .

Social insects offer powerful models to investigate the mechanistic foundation of elaborate individual behaviors comprising a cooperative community. Workers of the leafcutter ant genus provide an extreme example of behavioral segregation among many phenotypically distinct worker types. We utilize the complex worker system of to test the molecular underpinnings of behavioral programming and, in particular, the extent of plasticity to reprogramming.

Perioperative Acute Kidney Injury: Diagnosis, Prediction, Prevention, and Treatment.

In this review, the authors define acute kidney injury in the perioperative setting, describe the epidemiologic burden, discuss procedure-specific risk factors, detail principles of management, and highlight areas of ongoing controversy and research.

Decoding the brain: From neural representations to mechanistic models.

A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas, dedicated to different functions to varying extents, and their function relies on distributed circuits to continuously encode relevant environmental and body-state features, enabling other areas to decode (interpret) these representations for computing meaningful decisions and executing precise movements. Thus, the distributed brain can be thought of as a series of computations that act to encode and decode information.

Bronchial Blocker Versus Endobronchial Intubation in Young Children Undergoing One-Lung Ventilation: A Multicenter Retrospective Cohort Study.

Background: Thoracic surgery and one-lung ventilation in young children carry significant risks. Approaches to one-lung ventilation in young children include endobronchial intubation (mainstem intubation) and use of a bronchial blocker. We hypothesized that endobronchial intubation is associated with a greater prevalence of airway complications compared to use of a bronchial blocker.

A Comparison of Interpretable Machine Learning Approaches to Identify Outpatient Clinical Phenotypes Predictive of First Acute Myocardial Infarction.

Background: Acute myocardial infarctions are deadly to patients and burdensome to healthcare systems. Most recorded infarctions are patients' first, occur out of the hospital, and often are not accompanied by cardiac comorbidities. The clinical manifestations of the underlying pathophysiology leading to an infarction are not fully understood and little effort exists to use explainable machine learning to learn predictive clinical phenotypes before hospitalization is needed.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.

Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute because keypoint data are susceptible to high-frequency jitter that clustering algorithms can mistake for transitions between actions.

SuperAnimal pretrained pose estimation models for behavioral analysis.

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference of poses currently requires domain knowledge and manual labeling effort to build supervised models.

The Association of Oxygen Delivery and Transfusion on Cardiopulmonary Bypass with Acute Kidney Injury.

Objectives: To estimate whether the association of transfusion and acute kidney injury (AKI) has a threshold of oxygen delivery below which transfusion is beneficial but above which it is harmful.

Design: Retrospective study SETTING: Cardiovascular operating room and intensive care unit PARTICIPANTS: Patients undergoing cardiac surgery with continuous oxygen delivery monitoring during cardiopulmonary bypass INTERVENTIONS: None MEASUREMENTS AND MAIN RESULTS: Logistic regression was used to estimate the associations between oxygen delivery (mean, cumulative deficit, and bands of oxygen delivery), transfusion, and their interaction and AKI. A subgroup analysis of transfused and nontransfused patients with exact matching on cumulative oxygen deficit and time on bypass with adjustment for propensity to receive a transfusion using logistic regression.

Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study.

Background: Accurate projections of procedural case durations are complex but critical to the planning of perioperative staffing, operating room resources, and patient communication. Nonlinear prediction models using machine learning methods may provide opportunities for hospitals to improve upon current estimates of procedure duration.

Objective: The aim of this study was to determine whether a machine learning algorithm scalable across multiple centers could make estimations of case duration within a tolerance limit because there are substantial resources required for operating room functioning that relate to case duration.

Anesthesia Techniques for Vitreoretinal Surgery in the United States: A Report from the Multicenter Perioperative Outcomes Group Research Consortium.

Purpose: To explore the patterns of anesthesia use and their determinants during vitreoretinal (VR) surgeries in academic and community hospitals across the US, using data from the Multicenter Perioperative Outcomes Group (MPOG).

Design: A retrospective, multicenter, cohort study.

Methods: We queried the MPOG database of 107,066 patients undergoing VR surgeries.

Navigating Supervision and Interprofessional Relationships in Health Psychology.

Previous literature has focused on either individual models of supervision, developing trainees' interprofessional competencies, or on developing and maintaining interprofessional relationships outside of training. For psychologists in medical settings, these concepts are inextricably linked, and supervision must combine these professional practices to successfully meet the needs of psychology trainees, patients, and interprofessional colleagues, in an increasingly integrated healthcare landscape. This paper presents a model for advancing interprofessional collaborative practice competencies in supervision as health psychology trainees progress through the developmental stages of clinical competency, while supervising psychologists also maintain interprofessional relationships.

Real-World Data Analysis of CDK4/6 Inhibitor Therapy-A Patient-Centric Single Center Study.

Background: The quest to comprehend the real-world efficacy of CDK4/6 inhibitors (CDKis) in breast cancer continues, as patient responses vary significantly.

Methods: This single-center retrospective study evaluated CDKi use outside the trial condition from November 2016 to May 2020. Progression-free survival (PFS), time-to-treatment failure (TTF), short-term and prolonged treatment benefit (≥4 and ≥10 months), as well as prognostic and predictive markers were assessed with Kaplan-Meier and multivariate regression analyses.

Ethological computational psychiatry: Challenges and opportunities.

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g.

Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology.

Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. Within cardiac anesthesiology, challenges and opportunities for AI/ML to support patient care are presented by the vast amounts of electronic health data, which are collected rapidly, interpreted, and acted upon within the periprocedural area.

Generalizability of an acute kidney injury prediction model across health systems.

Delays in the identification of acute kidney injury (AKI) in hospitalized patients are a major barrier to the development of effective interventions to treat AKI. A recent study by Tomasev and colleagues at DeepMind described a model that achieved a state-of-the-art performance in predicting AKI up to 48 hours in advance. Because this model was trained in a population of US Veterans that was 94% male, questions have arisen about its reproducibility and generalizability.