Citations

Background: Approximately 50,000 neonates weighing under 1,500 grams are born annually in the United States, with 15–20% delivered in smaller community hospitals without a neonatal intensive care unit. These “outborn” infants face higher risks of adverse outcomes due to limited resources. Implementing tele-resuscitation services with targeted training workshops may enhance providers’ technical skills, confidence, and perceptions of feasibility and acceptability. Methods: In January 2023, Oklahoma Children’s Hospital launched a tele-resuscitation program offering real-time, audio-video support to community hospital staff. This service targeted neonates ≤32 weeks’ gestation, <1,500 g, and those >32 weeks requiring advanced resuscitation. Workshops were hosted to give community hospital providers a first hand experience with the technology, and it included background on tele-resuscitation, technical skills training, and neonatal resuscitation simulations incorporating telemedicine. Pre- and postworkshop surveys with Likert scale questions assessed attendees’ confidence in advanced resuscitation and feasibility of integrating tele-resuscitation into practice. Results: Briefly, 20 community providers attended two interprofessional workshops, including 5 physicians (25%), 11 nurses (55%), and 4 respiratory therapists (20%). One-quarter of attendees reported prior experience with telemedicine, and one attendee reported prior experience with tele-resuscitation. Following the workshops, participant confidence in performing tasks such as intubation, umbilical line placement, needle thoracentesis, and debriefing increased significantly ( p < 0.001 for all procedures). While ratings for the appeal and applicability of telemedicine to their practice showed no significant changes ( p = 0.267 and p = 0.056, respectively), the perceived feasibility of tele-resuscitation significantly improved after the workshop ( p = 0.029). Conclusions: Neonatal tele-resuscitation workshops improved providers’ perceptions of this service and increased their acceptance of such a program. Additionally, the skill sessions and simulated resuscitations increased their confidence with procedures commonly performed during advanced neonatal resuscitation.

Healthcare organizations face persistent tension between efficiency and human connection. Digital systems often fragment attention and unintentionally distance staff from patients. At Mayo Clinic, we developed a conversational artificial intelligence (AI) agent embedded in Microsoft Teams to address this challenge. Connecting and Communicating (CoCo) provides staff with communication guidance, drawing on institutional resources aligned with the Mayo Model of Communication (MMOC). Built in Microsoft Copilot Studio and shaped by clinicians and operational stakeholders, CoCo was designed to be values-aligned and usable within existing workflows. CoCo is an active, user-initiated tool used primarily before or after challenging interactions to support preparation, reflection, and communication planning. Staff valued CoCo as a “just-in-time coach” that reinforced empathy while reducing stress. From August 2025 through February 2026, CoCo supported 1,903 conversation sessions, with early descriptive analytics suggesting favorable satisfaction and response-quality signals. We share insights from development and deployment, along with practical recommendations, to support other organizations considering bespoke, values-aligned AI tools to enhance patient and staff experience. We distill practice-based implementation lessons around governance, human factors, and change management, and offer practical recommendations to inform similar efforts elsewhere.

Recognizing that antioxidant systems are impaired in the airways of premature infants, we used human fetal airway cells to explore the impact of oxygen on iron regulation and iron-mediated cell death (ferroptosis). We find moderate hyperoxia impairs antioxidant systems that prevent ferroptosis and dysregulates iron metabolism, while severe hyperoxia has a negative effect on mechanisms driving the early onset of ferroptosis. Inhibitors of ferroptosis decrease iron and lipid peroxidation, demonstrating links between oxygen and iron regulation in developing airways.

Apply It! By reading this article, you will: • Understand how commonly used cardiorespiratory fitness prediction equations can misclassify fitness in both high-performance and clinical populations. • Learn how common prediction equations were developed and why the population of prediction equation studies matters when interpreting estimated cardiorespiratory fitness and percent-predicted values. • Recognize how to critically assess the suitability of a given cardiorespiratory fitness equation based on your client’s or patient’s characteristics.

Abstract Large Language Models (LLMs) demonstrate strong capabilities in healthcare applications. However, recruiting human evaluators to assess usability and effectiveness becomes challenging in the large-scale LLM-generated outcomes. Existing metrics are often outdated for evaluating LLM-based healthcare technologies. This paper proposes a Research through Evaluation (RtE) approach that can refine metrics and improve assessments of LLM-generated outputs. We deploy RtE in a retrospective comparative study of prostate cancer patient inquiries, comparing responses from human clinical care teams with those generated by a GPT-4-based in-basket bot. Through three rounds of co-evaluations with clinical professionals and an LLM-as-graders study with four LLMs, the RtE method shows that starting with small, iterative evaluations helps human graders adjust metrics to suit their daily practices better. Additionally, in the LLM-as-graders study, LLMs exhibit similar behavior, preferring the in-basket bot's responses over the clinical care team. Our methodology and findings demonstrate that the RtE method can enhance the transparency and robustness of evaluation metrics for LLM-generated content, with the potential to generalize to evaluations in other clinical practices.

Abstract Background Sleep disturbances are common in Alzheimer's disease and have been reported in its preclinical stage. A detailed characterization of sleep disruption across the Alzheimer’s disease continuum is required to clarify its role as a potential therapeutic target and to evaluate the utility of sleep-related biomarkers. Methods Sleep architecture was characterized through detailed spectral and temporal analysis of the slow-wave activity using polysomnography across the Alzheimer’s disease continuum . Participants included: cognitively unimpaired individuals without abnormal Alzheimer’s disease biomarkers (n = 30), cognitively unimpaired individuals with abnormal Alzheimer’s disease biomarkers (n = 45), and individuals with mild cognitive impairment or mild dementia due to Alzheimer’s disease (n = 41). Associations between slow-wave features and longitudinal cognitive performance were tested in cognitively unimpaired participants. Sex differences were also explored. Results A stepwise increased wake after sleep onset and latency to rapid eye movement sleep was observed, alongside reduced spectral power of slow-wave activity and a progressive flattening of slow-waves across the Alzheimer’s disease continuum in both sexes. In cognitively unimpaired individuals, higher slow-wave slope and greater peak-to-peak amplitude were associated with better cognitive trajectories over time. Conclusions Sleep macro- and microarchitecture are altered early and progressively deteriorate across the Alzheimer’s disease continuum . Slow-wave features relate to cognitive trajectories in preclinical Alzheimer’s disease, with exploratory analyses suggesting stronger associations in men. These findings support slow-wave morphology as a candidate non-invasive marker for future sleep-based intervention studies.