Gestational Diabetes-Screening, Prevalence and Postpartum Diabetes: Population-Based Cohort Study.

Aims: To evaluate GDM screening compliance and prevalence, and the association between gestational glucose intolerance and 5-year postpartum diabetes mellitus (DM).

Materials And Methods: We used population-based data from three Israeli health maintenance organisations (HMOs), covering 75% of all births in 2016. GDM screening followed a two-step approach: a 50-g 1-h oral glucose challenge test (OGCT), followed by a 100-g 3-h oral glucose tolerance test (OGTT) using Carpenter-Coustan criteria.

Prevalence, Risk Factors, and Outcomes of Chronic Kidney Disease in Patients With Systemic Lupus Erythematosus With and Without Lupus Nephritis.

Objective: Chronic kidney disease (CKD) has significant clinical and therapeutic implications. This study assessed CKD prevalence, risk factors, and long-term outcomes in patients with systemic lupus erythematosus (SLE), both with and without lupus nephritis (LN).

Methods: This single-center, retrospective, medical records review study, conducted between 2014 and 2023, included adult patients with SLE.

Navigating the AI Landscape in Medical Imaging: A Critical Analysis of Technologies, Implementation, and Implications.

The growing volume and complexity of medical imaging outpaces the available radiologist workforce, risking timely diagnosis. Comprehensive artificial intelligence (AI) that integrates multimodal imaging data, clinical notes, and large language models has the potential to support radiologists. Accordingly, the U.

When investigator meets large language models: a qualitative analysis of cancer patient decision-making journeys.

Large language models (LLMs) are transforming the landscape of healthcare research, yet their role in qualitative analysis remains underexplored. This study compares human-led and LLM-assisted approaches to analyzing cancer patient narratives, using 33 semi-structured interviews. We conducted three parallel analyses: investigator-led thematic analysis, ChatGPT-4o, and Gemini Advance Pro 1.

ADHD and Parkinson's disease: unraveling the link and implications for early intervention.

Background: Attention deficit/hyperactivity disorder (ADHD) and Parkinson's disease (PD) are neurodevelopmental and neurodegenerative conditions respectively, involving alterations in dopamine signaling pathways. Emerging evidence suggests ADHD may be a potential risk factor for earlier PD onset. However, rigorous investigation of this association is still lacking.

Efficacy of diabetes specialist nurse-led remote care in community clinics: a retrospective cohort study.

Background: This study evaluates the impact of a diabetes specialist nurse intervention on clinical outcomes and healthcare utilization among patients with diabetes.

Methods: A cohort of 452 patients was observed from 2019 to 2022. Clinical metrics such as HbA1C levels and BMI, as well as healthcare utilization patterns, were analyzed before and after the intervention.

Comparison of CT referral justification using clinical decision support and large language models in a large European cohort.

Background: Ensuring appropriate use of CT scans is critical for patient safety and resource optimization. Decision support tools and artificial intelligence (AI), such as large language models (LLMs), have the potential to improve CT referral justification, yet require rigorous evaluation against established standards and expert assessments.

Aim: To evaluate the performance of LLMs (Generation Pre-trained Transformer 4 (GPT-4) and Claude-3 Haiku) and independent experts in justifying CT referrals compared to the ESR iGuide clinical decision support system as the reference standard.

Personalized Approaches to Diabetic Foot Care: The Impact of Ethnic and Socioeconomic Disparities.

: To explore the impact of ethnic and socioeconomic disparities on diabetic foot ulcer (DFU) care and outcomes, emphasizing the need for personalized treatment approaches tailored to diverse patient populations. : This retrospective observational study analyzed 1409 patients hospitalized with DFUs between 2016 and 2023 at a tertiary medical center. Data extracted from electronic medical records included demographics, socioeconomic status (SES), clinical variables, and healthcare utilization.

Leveraging natural language processing to elucidate real-world clinical decision-making paradigms: A proof of concept study.

Background: Understanding how clinicians arrive at decisions in actual practice settings is vital for advancing personalized, evidence-based care. However, systematic analysis of qualitative decision data poses challenges.

Methods: We analyzed transcribed interviews with Hebrew-speaking clinicians on decision processes using natural language processing (NLP).

Bridging generational gaps in medication safety: insights from nurses, students, and generative AI models.

Background: This study investigated medication dose calculation accuracy among nurses, nursing students, and Generative AI (GenAI) models, examining error prevention strategies across generational cohorts.

Methods: A cross-sectional study was conducted from June to August 2024, involving 101 pediatric/neonatal nurses, 91 nursing students, and four GenAI models. Participants completed a questionnaire on calculation proficiency and provided recommendations for error prevention.

Augmenting Community Nursing Practice With Generative AI: A Formative Study of Diagnostic Synergies Using Simulation-Based Clinical Cases.

Objective: To compare the diagnostic accuracy and clinical decision-making of experienced community nurses versus state-of-the-art generative AI (GenAI) systems for simulated patient case scenarios.

Methods: In the months of 5 to 6/2024, 114 community Israeli nurses completed a questionnaire including 4 medical case studies. Responses were also collected from 3 GenAI models (ChatGPT-4, Claude 3.

Can large language models assist with pediatric dosing accuracy?

Background And Objective: Medication errors in pediatric care remain a significant healthcare challenge despite technological advancements, necessitating innovative approaches. This study aims to evaluate Large Language Models' (LLMs) potential in reducing pediatric medication dosage calculation errors compared to experienced nurses.

Methods: This cross-sectional study (June-August 2024) involved 101 nurses from pediatric and neonatal departments and three LLMs (ChatGPT-4o, Claude-3.

Ethnicity, Patient Satisfaction, and Clinical Outcomes among Israeli Dialysis Recipients.

Key Points: Growing interest in health-related quality of life underscores the need to explore patient satisfaction among diverse dialysis populations. In this study, patient ethnicity in Israel showed no significant effect on satisfaction, mortality, or transplantation outcomes. Dialysis vintage and patient age were key predictors of satisfaction and survival, highlighting areas for targeted interventions.

Ensuring continuity of care in times of crisis: A longitudinal analysis of dementia care delivery during armed conflict.

Background: Individuals with dementia are particularly vulnerable during emergency situations due to challenges with cognition, mobility, and daily functioning. However, little is known about how disruptive events may specifically impact the health of those with dementia.

Objective: To evaluate changes in health outcomes for individuals with and without dementia surrounding the Israel-Gaza war in October 2023.

Risk Factors for Major Bleeding among Patients with Chronic Kidney Disease Treated with Acetylsalicylic Acid.

Introduction: Individuals with chronic kidney disease (CKD) are at increased risk of thrombotic events and bleeding. Acetylsalicylic acid (ASA), an effective antiplatelet agent, is one of the most frequently used medications for both primary and secondary prevention of cardiovascular disease (CVD). However, it can also contribute to bleeding events due to its inherent antiplatelet effect.

Skeletal muscle size and quality in healthy kidney donors, normal range and clinical associations.

The gold standard to estimate muscle mass and quality is computed tomography (CT) scan. Lower mass and density (intramuscular fat infiltration) of skeletal muscles are markers of sarcopenia, associated with increased mortality risk, impaired physical function, and poorer prognosis across various populations and medical conditions. We aimed to describe standard reference values in healthy population, prospective kidney donors, and correlate clinical parameters to muscle mass and density.

Computed tomography referral guidelines adherence in Europe: insights from a seven-country audit.

Background: Ensuring appropriate computed tomography (CT) utilization optimizes patient care while minimizing radiation exposure. Decision support tools show promise for standardizing appropriateness.

Objectives: In the current study, we aimed to assess CT appropriateness rates using the European Society of Radiology (ESR) iGuide criteria across seven European countries.

Optimizing outcomes: A retrospective analysis of discharge safety for elderly mTBI patients on anticoagulation therapy.

Background: Mild traumatic brain injuries (mTBIs) pose a significant risk, particularly in the elderly population on anticoagulation therapy. The safety of discharging these patients from the emergency department (ED) with a negative initial computed tomography (CT) scan has been debated due to the risk of delayed intracranial hemorrhage (d-ICH).

Objective: To compare outcomes, including d-ICH, between elderly patients on anticoagulation therapy presenting with mTBI who were admitted versus discharged from the ED after an initial negative head CT scan.

Augmenting intensive care unit nursing practice with generative AI: A formative study of diagnostic synergies using simulation-based clinical cases.

Background: As generative artificial intelligence (GenAI) tools continue advancing, rigorous evaluations are needed to understand their capabilities relative to experienced clinicians and nurses. The aim of this study was to objectively compare the diagnostic accuracy and response formats of ICU nurses versus various GenAI models, with a qualitative interpretation of the quantitative results.

Methods: This formative study utilized four written clinical scenarios representative of real ICU patient cases to simulate diagnostic challenges.

Treating wartime injuries amidst attack: insights from a medical facility on the edge of combat.

Background: Providing emergency care during conflict poses unique challenges for frontline hospitals. Barzilai Medical Center (BUMCA) in Ashkelon, Israel is a Level I trauma center located close to the Gaza border. During the November 2023 escalation of conflict, BUMCA experienced surging numbers of civilian and military trauma patients while also coming under rocket fire.

Evaluating the Accuracy and Impact of the ESR-iGuide Decision Support Tool in Optimizing CT Imaging Referral Appropriateness.

Radiology referral quality impacts patient care, yet factors influencing quality are poorly understood. This study assessed the quality of computed tomography (CT) referrals, identified associated characteristics, and evaluated the ESR-iGuide clinical decision support tool's ability to optimize referrals. A retrospective review analyzed 300 consecutive CT referrals from an acute care hospital.