Development of a Novel Web-Based Intervention Targeting Pain-Related Outcomes in Individuals With Chronic Orofacial Pain: Protocol for a Mixed Methods Study.

Background: Chronic orofacial pain (COP) is common, costly, and associated with substantial pain interference and emotional distress. Psychosocial treatments for COP are scarce and limited (eg, rely on talking, which is often painful for this population; require intensive resources, limiting scalability). Here, we describe the study protocol for developing Face-Forward-Web, a "talk-free" web-based mind-body intervention for patients with COP.

Exploring temperature trajectories in emergency department sepsis patients.

Introduction: Sepsis presents a global challenge to emergency departments (ED) due to its varied presentation and life-threatening outcomes. The quick Sequential Organ Failure Assessment score's introduction expanded sepsis diagnostics beyond the traditional Systemic Inflammatory Response Syndrome, which considered body temperature. However, body temperature remains a vital clinical marker.

The Elastic Electronic Health Record: A Five-Tiered Framework for Applying Artificial Intelligence to Electronic Health Record Maintenance, Configuration, and Use.

Properly configuring modern electronic health records (EHRs) has become increasingly challenging for human operators, failing to fully meet the efficiency and cost-saving potential seen with the digitization of other sectors. The integration of artificial intelligence (AI) offers a promising solution, particularly through a comprehensive governance approach that moves beyond front-end enhancements such as user- and patient-facing copilots. These copilots, although useful, are limited by the underlying EHR configuration, leading to inefficiencies and high maintenance costs.

A Systematic Review of Self-Reported Pain Rating Scales for Children and Adolescents.

Background: Pain assessment in pediatric populations via self-report tools pose unique challenges given the patients' cognitive abilities and developmental status; however, the accurate measurement of pediatric pain is crucial in improving patient outcomes.

Objectives: This review evaluates recent medical literature to better understand potential correlations and concordance exhibited by self-reported pain intensity assessment tools for children and adolescents in addition to assessing the viability and utility of electronic delivery modalities.

Study Design: Systematic review without meta-analysis.

Development and validation of the discharge severity index for post-emergency department hospital readmissions.

Introduction: Hospital readmissions often result from a combination of factors, including inadequate follow-up care, poor discharge planning, patient non-adherence, and social determinants of health (SDOH) that impact access to healthcare and follow-up resources, many of which are beyond provider control. Enhanced post-discharge strategies, including risk stratification, are essential. This study aims to develop and validate the Discharge Severity Index (DSI) to predict readmission risk and optimize resource allocation for effective follow-up care.

Development and validation of a machine learning framework for improved resource allocation in the emergency department.

Objective: The Emergency Severity Index (ESI) is the most commonly used system in over 70% of all U.S. emergency departments (ED) that uses predicted resource utilization as a means to triage [1], Mistriage, which includes both undertriage and overtriage has been a persistent issue, affecting 32.

A copper missile-triggered power coalescence and death vortex within tumor cell mitochondria for synergistic cuproptosis/phototherapy/chemotherapy.

The importance of copper homeostasis in mitochondria and copper-triggered modality of mitochondrial cell death have been confirmed. However, the existing copper-based nanoplatforms are focused on synergistic therapies while the intracellular therapeutic targets are relatively scattered. Effective integration of all targets within mitochondria to generate power coalescence remains a challenge.

Using a Combination of Novel Research Tools to Understand Social Interaction in the Model for Fragile X Syndrome.

Fragile X syndrome (FXS), the most common monogenic cause of inherited intellectual disability and autism spectrum disorder, is caused by a full mutation (>200 CGG repeats) in the Fragile X Messenger Ribonucleoprotein 1 () gene. Individuals with FXS experience various challenges related to social interaction (SI). Animal models, such as the model for FXS where the only ortholog of human () is mutated, have played a crucial role in the understanding of FXS.

Discharge process for patients experiencing homelessness in the emergency department: A thematic qualitative study.

People experiencing homelessness are more likely to utilize emergency departments than their non-homeless counterparts. However, obtaining a bed in a homeless shelter for patients can be complex. To better understand the challenges of finding a safe discharge plan for homeless patients in the emergency department, our team conducted interviews with emergency department social workers and homeless shelter case managers in the Boston area.

Biomedical text readability after hypernym substitution with fine-tuned large language models.

The advent of patient access to complex medical information online has highlighted the need for simplification of biomedical text to improve patient understanding and engagement in taking ownership of their health. However, comprehension of biomedical text remains a difficult task due to the need for domain-specific expertise. We aimed to study the simplification of biomedical text via large language models (LLMs) commonly used for general natural language processing tasks involve text comprehension, summarization, generation, and prediction of new text from prompts.

Impact of medical technologies may be predicted using constructed graph bibliometrics.

Scientific research is driven by allocation of funding to different research projects based in part on the predicted scientific impact of the work. Data-driven algorithms can inform decision-making of scarce funding resources by identifying likely high-impact studies using bibliometrics. Compared to standardized citation-based metrics alone, we utilize a machine learning pipeline that analyzes high-dimensional relationships among a range of bibliometric features to improve the accuracy of predicting high-impact research.

Entropy removal of medical diagnostics.

Shannon entropy is a core concept in machine learning and information theory, particularly in decision tree modeling. To date, no studies have extensively and quantitatively applied Shannon entropy in a systematic way to quantify the entropy of clinical situations using diagnostic variables (true and false positives and negatives, respectively). Decision tree representations of medical decision-making tools can be generated using diagnostic variables found in literature and entropy removal can be calculated for these tools.

Predictors of Mortality in Extracorporeal Membrane Oxygenation Support Patients Following Major Trauma.

Introduction: The usage of extracorporeal membrane oxygenation (ECMO) in trauma patients has increased significantly within the past decade. Despite increased research on ECMO application in trauma patients, there remains limited data on factors predicting morbidity and mortality outcome. Therefore, the primary objective of this study is to describe patient characteristics that are independently associated with mortality in ECMO therapy in trauma patients, to further guide future research.

PlanNet: A Generative Model for Component-Based Plan Synthesis.

We propose a novel generative model named as PlanNet for component-based plan synthesis. The proposed model consists of three modules, a wave function collapse algorithm to create large-scale wireframe patterns as the embryonic forms of floor plans, and two deep neural networks to outline the plausible boundary from each squared pattern, and meanwhile estimate the potential semantic labels for the components. In this manner, we use PlanNet to generate a large-scale component-based plan dataset with 10 K examples.

Does temperature favour the spread of Raphidiopsis raciborskii, an invasive bloom-forming cyanobacterium, by altering cellular trade-offs?

As a tropical filamentous cyanobacterium, Raphidiopsis raciborskii has attracted much attention due to its expansion and toxin production. However, the mechanisms of its expansion to temperate regions have not been studied in detail. To address the potential strategies, the physiological and metabolomic profiles of R.

Network Analysis of Academic Medical Center Websites in the United States.

Healthcare resources are published annually in repositories such as the AHA Annual Survey Database. However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format.

Digital Impact Factor: A Quality Index for Educational Blogs and Podcasts in Emergency Medicine and Critical Care.

Study Objective: Given the popularity of educational blogs and podcasts in medicine, learners and educators need tools to identify trusted and impactful sites. The Social Media Index was a multi-sourced formula to rank the effect of emergency medicine and critical care blogs. In 2022, a key data point for the Social Media Index became unavailable.

Influence of ICU Surge and Capacity on COVID Mortality Across U.S. States and Regions During the COVID-19 Pandemic.

Background: To describe the influence of COVID-19 caseload surges and overall capacity in the intensive care unit (ICU) on mortality among US population and census divisions.

Methods: A retrospective analysis of the national COVID ActNow database between January 1, 2021 until March 1, 2022. The main outcome used was COVID-19 weekly mortality rates, which were calculated and incorporated into several generalized estimation of effects models with predictor variables that included ICU bed capacity, as well as ICU capacity used by COVID cases while adjusting for ratios of vaccinations in populations, case density, and percentage of the population over the age of 65.

PPE needs in the United States during the COVID-19 pandemic: An analysis using the GetUsPPE online platform.

The United States is heavily impacted by the COVID-19 pandemic starting in 2020. Demand for personal protective equipment (PPE) domestically, along with global surge in demand for PPE during the pandemic, overwhelmed supply chains, leading to acute PPE shortages. This article analyzes the PPE supply and demand in the United States by employing data collected by GetUsPPE, a data hub used throughout the pandemic to coordinate support efforts, including connecting facilities in need of PPE with donated supplies.

Correction: The Story of #GetMePPE and GetUsPPE.org to Mobilize Health Care Response to COVID-19 : Rapidly Deploying Digital Tools for Better Health Care.

[This corrects the article DOI: 10.2196/20469.].