Şule Zuhal Gürsoy Durak, Özlem Tolu Kendir, Nilgün Erkek
et al.
Introduction: As the benefit of many pharmacologic treatments for bronchiolitis is a source of debate, investigations of more effective, easy-to-apply treatment modalities of acute bronchiolitis remain up-to-date.
Methods: In this study, nebulised salbutamol plus standard oxygen (S) and HFNC (HF) therapies were administered to children younger than two years of age, with a respiratory clinical score (RCS) ≥4 points, who presented with a first episode of acute bronchiolitis.
Results: The mean age of 72 patients was 7.8±0.4, and 59.7% were younger than six months. The mean RCS of the patients at admission was 8.42±2.026 points. A significant decrease was observed in the mean RCS scores evaluated at 1-2-4-8 hours, from the first hour (p<0.05). The mean length of hospital stay and duration of oxygen therapy were 70±64.6 (4-288) and 67.7±62.2 (4-264) hours. Within the first few days after discharge, 50% of the patients returned to the pediatric emergency department (PED). The mean RCS showed a difference in favour of the HF group from the second hour of treatment (p=0.002). Expected improvement was not observed in 17.1% of the patients in the S group only, thus HF should be added. Patients in the HF group and patients in whom HF was added to S had higher hospitalisation rates (p=0.017), longer hospital stays (p=0.002), and longer duration of oxygen therapy (p=0.001). Re-admission to PED after discharge was observed in 64.2% of the cases in the S group only (p<0.001).
Conclusion: In this study, it may be said that HFNC treatment provides earlier and faster clinical improvement in children with bronchiolitis and reduces re-admissions related to the same disease.
Peer review remains a cornerstone in academia, yet it frequently falls short in fostering joint progress and well-being. While peer review primarily emphasizes scientific rigor, it often lacks the empathy essential for supporting and encouraging all peers involved. In this experience report, I aim to highlight that peer review is a practice that demands both scientific care for quality and collegial welfare for the joint progress and well-being of all peers involved, including authors, co-reviewers, workshop or conference organizers, and journal editors. Drawing on my ten years of experience in academia, I propose the ethos of the PEERfect REVIEWer, grounded in the two core values: Scientific care and collegial welfare. Through reflection shaped by professional exchanges with colleagues, consideration of literature, and an examination of both self-authored and received reviews, I formulated an accompanying guideline with 16 practical recommendations to guide reviewers in their actions to achieve these two values. The ethos of the PEERfect REVIEWer and its accompanying guideline help reviewers in upholding high scientific standards and conducting peer review in a constructive, supportive, respectful, and timely manner. They demonstrate that scientific rigor and empathy are complementary forces that promote impactful peer review practice. By placing scientific care and collegial welfare at the core of peer review, this experience report reaffirms the importance of scientific rigor while also advocating for greater attention to empathy. It invites reviewers to reconsider their role not merely as gatekeepers but as partners in the academic journey of each peer involved. The PEERfect REVIEWer is both a caretaker of quality and a steward of joint progress and well-being - as truly impactful peer review practice requires scientific rigor and empathy in equal measure.
Andre Paulino de Lima, Paula Castro, Suzana Carvalho Vaz de Andrade
et al.
There are challenges that must be overcome to make recommender systems useful in healthcare settings. The reasons are varied: the lack of publicly available clinical data, the difficulty that users may have in understanding the reasons why a recommendation was made, the risks that may be involved in following that recommendation, and the uncertainty about its effectiveness. In this work, we address these challenges with a recommendation model that leverages the structure of psychometric data to provide visual explanations that are faithful to the model and interpretable by care professionals. We focus on a narrow healthcare niche, gerontological primary care, to show that the proposed recommendation model can assist the attending professional in the creation of personalised care plans. We report results of a comparative offline performance evaluation of the proposed model on healthcare datasets that were collected by research partners in Brazil, as well as the results of a user study that evaluates the interpretability of the visual explanations the model generates. The results suggest that the proposed model can advance the application of recommender systems in this healthcare niche, which is expected to grow in demand , opportunities, and information technology needs as demographic changes become more pronounced.
Introduction: Throughout graduate medical education (GME), it is crucial for learners to not only develop the skills necessary to manage a wide variety of medical conditions, but also to foster personal development and to gain a deeper understanding of the complex and multifaceted needs of our patients. We often refer patients to community sites to address needs such as homelessness, hunger, and domestic violence; however, we frequently make these referrals with only a superficial understanding of what each resource entails. Methods: To address this issue, our department integrated a two-day Community Engagement Retreat into our curriculum. Twenty-two first-year residents participated in small group visits to three or four community organizations. There, residents engaged with community workers and the public to learn about the services each program offers. This was followed by a session of focused reflection and discussion on how to integrate this new knowledge into our care for patients in the emergency department. At the conclusion of the experience, residents completed an anonymous survey with a response rate of 77.3%. Results: The results suggest that participants found the sessions highly useful, with 98.6% of residents reporting that they “agreed” or “strongly agreed” that the experiences at the community sites would better allow them to care for patients. They further stated that the program was one of the most impactful elements of their training and highly recommended it to future learners. Conclusion: This initiative demonstrates the importance and utility of a novel, structured community engagement to begin to address this deficiency in GME and improve patient care.
Medicine, Medical emergencies. Critical care. Intensive care. First aid
IMPORTANCE:. Propofol is a first-line sedative for adults receiving invasive mechanical ventilation (IMV). However, it can contribute to hemodynamic instability, especially during intubation. The magnitude, timing, risk factors, and variability of sedation-associated mean arterial pressure (MAP) changes remain poorly characterized in ICU settings.
OBJECTIVES:. To quantify MAP changes following propofol sedation, identify risk factors for hemodynamic instability, and characterize associated interventions.
DESIGN:. Retrospective cohort study. The primary outcome was MAP change within 2 hours following sedation. Secondary outcomes included vasopressor use and hypotension (MAP ≤ 60 mm Hg). Mixed-effects modeling was used to account for individual patient differences.
SETTING AND PARTICIPANTS:. Adults (≥ 18 yrs old) who required IMV and received greater than or equal to 6 consecutive hours of propofol infusion, between May 5th, 2018, and July 31st, 2024, in 11 ICUs across the Mayo Clinic, spanning 5 hospitals in 4 states.
MAIN OUTCOMES AND MEASURES:. The primary outcome was the change in MAP within 2 hours following the initiation of propofol-based sedation.
RESULTS:. Across 16,418 patients, 25.2% were on vasopressors before sedation initiation. Among the remaining 12,281 patients, 40.3% required vasopressors and 7.7% experienced hypotension within 2 hours of sedation. Propofol-based sedation was associated with a MAP reduction within the first 30 minutes (–6.58 mm Hg; 95% CI, –6.85 to –6.32; p < 0.001). There was substantial interpatient variability in both baseline MAP, and MAP decline after sedation (9.5 and 40.9% between-patient differences, respectively). Higher Sequential Organ Failure Assessment (SOFA) scores (–0.31 mm Hg/point), older age (–0.04 mm Hg/yr), and male sex (–0.47 mm Hg) were associated with lower MAP. Patients with higher illness severity experienced progressively greater MAP decline over time (–0.20 mm Hg/hr/SOFA point; p < 0.001).
CONCLUSIONS AND RELEVANCE:. Propofol-based sedation was associated with clinically significant hemodynamic effects requiring intervention in the early post-intubation period. The marked interpatient variability in hemodynamic responses highlights the importance of personalized management approaches, including risk stratification based on age, sex, and illness severity.
Medical emergencies. Critical care. Intensive care. First aid
Kazuya Kikutani, Mitsuaki Nishikimi, Michihito Kyo
et al.
Abstract Background Among patients with sepsis admitted to the intensive care unit (ICU), a substantial proportion require mechanical ventilation, and a subset eventually undergo tracheostomy. Early identification of patients at high risk for tracheostomy may facilitate timely decision-making and improve clinical communication. Methods We conducted a nationwide, retrospective study using the Japanese Intensive care PAtient Database (JIPAD). Adult patients with sepsis (Sequential Organ Failure Assessment score of ≥ 2, excluding viral pneumonia) who required mechanical ventilation between 2018 and 2022 were included. The primary outcome was tracheostomy within 14 days of ICU admission. Seventy-five variables available within 24 h of ICU admission were collected. Using least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation, we selected predictors to build a multivariable logistic regression model (Sepsis Tracheostomy early Prediction [STeP] model). A simplified scoring system (STeP score) was also derived. Predictive performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) in a temporally independent validation cohort. Results Among 7357 eligible patients (training: 5374; validation: 1983), 1013 (13.8%) underwent tracheostomy. The STeP model, based on 8 LASSO-selected variables, demonstrated good discrimination (AUC: 0.76 in training, 0.74 in validation). The simplified STeP score (range, 0–17), derived from the same predictors, achieved an AUC of 0.73 in the validation cohort. Patients were stratified into low (≤ 2 points), moderate (3–6 points), and high (≥ 7 points) risk groups for tracheostomy, with corresponding tracheostomy rates of 4.0%, 13.6%, and 27.1%, respectively. Conclusions We developed and validated a robust prediction model and simplified risk score (STeP score) for tracheostomy within 14 days in ICU patients with sepsis. Early risk stratification using variables available within 24 h may support timely tracheostomy planning. A web-based calculator is publicly available to facilitate bedside implementation.
Medical emergencies. Critical care. Intensive care. First aid
Yuing Sun, Sam Addison Ankenbauer, Zhifan Guo
et al.
Aging in place refers to the enabling of individuals to age comfortably and securely within their own homes and communities. Aging in place relies on robust infrastructure, prompting the development and implementation of both human-led care services and information and communication technologies to provide support. Through a long-term ethnographic study that includes semi-structured interviews with 24 stakeholders, we consider these human- and technology-driven care infrastructures for aging in place, examining their origins, deployment, interactions with older adults, and challenges. In doing so, we reconsider the value of these different forms of older adult care, highlighting the various issues associated with using, for instance, health monitoring technology or appointment scheduling systems to care for older adults aging in place. We suggest that technology should take a supportive, not substitutive role in older adult care infrastructure. Furthermore, we note that designing for aging in place should move beyond a narrow focus on independence in one's home to instead encompass the broader community and its dynamics.
Electronic health records (EHRs) provide comprehensive patient data which could be better used to enhance informed decision-making, resource allocation, and coordinated care, thereby optimising healthcare delivery. However, in mental healthcare, critical information, such as on risk factors, precipitants, and treatment responses, is often embedded in unstructured text, limiting the ability to automate at scale measures to identify and prioritise local populations and patients, which potentially hinders timely prevention and intervention. We describe the development and proof-of-concept implementation of VIEWER, a clinical informatics platform designed to enhance direct patient care and population health management by improving the accessibility and usability of EHR data. We further outline strategies that were employed in this work to foster informatics innovation through interdisciplinary and cross-organisational collaboration to support integrated, personalised care, and detail how these advancements were piloted and implemented within a large UK mental health National Health Service Foundation Trust to improve patient outcomes at an individual patient, clinician, clinical team, and organisational level.
Background Telemedicine has the potential to provide secure and cost-effective healthcare at the touch of a button. Nephrotic syndrome is a chronic childhood illness involving frequent relapses and demands long/complex treatment. Hence, developing a remote means of doctor-patient interface will ensure the provision of quality healthcare to patients. Methods The Utsarjan mobile App framework was built with Flutter that enables cross-platform development (Android, iOS, Windows) with speed, smoothness, and open-source benefits. The frontend uses Dart for user interaction, while the backend employs Node.js, Express, and NGINX for APIs, load balancing and high performance. MongoDB ensures a flexible database, Bcrypt secures passwords, PM2 handles deployment, uptime and logs, while Firebase Cloud Messaging powers free push notifications. Results Utsarjan (means excretion) is a multi-functional smartphone application for giving nephrotic care and real-time assistance to all patients (especially those in rural regions and/or who do not have access to specialists). It helps patients and doctors by ensuring opportune visits, recording each clinical test/parameter and improving medication adherence. It gives a graphical visualization of relapses, medicine dosage as well as different anthropometric parameters (urine protein, BP, height and weight). This is the first nephrotic care App that enables prompt access to doctor's advice. Conclusions Utsarjan is a mobile App to provide kidney care and real-time assistance to children with nephrotic syndrome. It gives a graphical overview of changes in a patient's health over the long course of treatment. This will assist doctors in appropriately modifying the treatment regimen. Consequently, it will (hopefully) lead to the prevention of relapses and/or complications.
Bavana Durgapraveen, Sornaraj Sivasankaran, Abhinand Balachandran
et al.
The rapid expansion of asynchronous remote care has intensified provider workload, creating demand for AI systems that can assist clinicians in managing patient queries more efficiently. The MEDIQA-WV 2025 shared task addresses this challenge by focusing on generating free-text responses to wound care queries paired with images. In this work, we present two complementary approaches developed for the English track. The first leverages a mined prompting strategy, where training data is embedded and the top-k most similar examples are retrieved to serve as few-shot demonstrations during generation. The second approach builds on a metadata ablation study, which identified four metadata attributes that consistently enhance response quality. We train classifiers to predict these attributes for test cases and incorporate them into the generation pipeline, dynamically adjusting outputs based on prediction confidence. Experimental results demonstrate that mined prompting improves response relevance, while metadata-guided generation further refines clinical precision. Together, these methods highlight promising directions for developing AI-driven tools that can provide reliable and efficient wound care support.
Learning models capable of providing reliable predictions in the face of adversarial actions has become a central focus of the machine learning community in recent years. This challenge arises from observing that data encountered at deployment time often deviate from the conditions under which the model was trained. In this paper, we address deployment-time adversarial actions and propose a versatile, well-principled framework to evaluate the model's robustness against attacks of diverse types and intensities. While we initially focus on Support Vector Regression (SVR), the proposed approach extends naturally to the broad domain of learning via relaxed optimization techniques. Our results enable an assessment of the model vulnerability without requiring additional test data and operate in a distribution-free setup. These results not only provide a tool to enhance trust in the model's applicability but also aid in selecting among competing alternatives. Later in the paper, we show that our findings also offer useful insights for establishing new results within the out-of-distribution framework.
INTRODUCTION: Older adults with early-stage dementia often retain procedural memory, enabling continued use of familiar technologies. Additionally, symbolic anchors such as photos or personalized content may serve as memory cues to reinforce digital engagement. This study explores how these mechanisms support technology use in dementia care within the South Korean context. METHODS: We conducted in-depth interviews with 11 professional caregivers of community-dwelling older adults with cognitive decline. Grounded theory methods guided the analysis, using iterative coding and constant comparison to identify emergent themes. RESULTS: Caregivers reported that familiar digital routines (e.g., taking photos) persisted through procedural memory. Symbolic anchors such as family photos or recognizable icons enhanced interaction and emotional engagement. However, unfamiliar or anthropomorphic technologies often triggered fear or symbolic resistance. DISCUSSION: Findings highlight the dual role of procedural memory and symbolic anchors in sustaining digital engagement. Designing culturally responsive and cognitively accessible technologies may enhance autonomy and well-being in dementia care. Keywords: procedural memory, symbolic anchors, dementia care, digital engagement, older adults, cultural adaptation, caregiving technologies
The medical ecosystem consists of the training of new clinicians and researchers, the practice of clinical medicine, and areas of adjacent research. There are many aspects of these domains that could benefit from the application of task automation and programmatic assistance. Machine learning and artificial intelligence techniques, including large language models (LLMs), have been promised to deliver on healthcare innovation, improving care speed and accuracy, and reducing the burden on staff for manual interventions. However, LLMs have no understanding of objective truth that is based in reality. They also represent real risks to the disclosure of protected information when used by clinicians and researchers. The use of AI in medicine in general, and the deployment of LLMs in particular, therefore requires careful consideration and thoughtful application to reap the benefits of these technologies while avoiding the dangers in each context.
Hamed Abdollahi, Hassan Tavakoli, Yousef Mojtahedi
et al.
Background: The COVID-19 pandemic is a traumatic event with a global impact, predicted to increase depression, anxiety, substance use, sadness, and loneliness. This study was conducted to evaluate the scale of depression, anxiety, and stress among patients infected with the COVID-19 virus.
Methods: This cross-sectional study was conducted between April 2019 and April 2022. According to the conditions of the study space, available sampling was selected. In addition to demographic characteristics, a questionnaire related to stress, anxiety, and depression (DASS-21) was used to collect data. Then, the collected data were entered into SPSS software for analysis, and Pearson's correlation was used to check the relationship between the variables, with the significance level (P-value) reported.|
Results: Out of a total of 714 participants, 26.1% had higher scores in depression, 37.5% in anxiety, and 15.7% in stress. In this way, two-thirds of the studied population on the depression and anxiety scale and almost half of the studied population on the stress scale experienced degrees of these disorders from mild to very severe during the period of COVID-19 infection. The scores of each subcategory of depression, anxiety, and stress are significantly correlated with each other, which shows that people who have a higher score in one subcategory also have a higher score in two subcategories.
Conclusion: It seems that COVID-19 has an obvious effect on the mental health of people. Thus, more policies and attention are needed in this field to manage the disease.
Anesthesiology, Medical emergencies. Critical care. Intensive care. First aid
Óscar Escudero-Arnanz, Cristina Soguero-Ruiz, Joaquín Álvarez-Rodríguez
et al.
Antimicrobial Resistance represents a significant challenge in the Intensive Care Unit (ICU), where patients are at heightened risk of Multidrug-Resistant (MDR) infections-pathogens resistant to multiple antimicrobial agents. This study introduces a novel methodology that integrates Gated Recurrent Units (GRUs) with advanced intrinsic and post-hoc interpretability techniques for detecting the onset of MDR in patients across time. Within interpretability methods, we propose Explainable Artificial Intelligence (XAI) approaches to handle irregular Multivariate Time Series (MTS), introducing Irregular Time Shapley Additive Explanations (IT-SHAP), a modification of Shapley Additive Explanations designed for irregular MTS with Recurrent Neural Networks focused on temporal outputs. Our methodology aims to identify specific risk factors associated with MDR in ICU patients. GRU with Hadamard's attention demonstrated high initial specificity and increasing sensitivity over time, correlating with increased nosocomial infection risks during prolonged ICU stays. XAI analysis, enhanced by Hadamard attention and IT-SHAP, identified critical factors such as previous non-resistant cultures, specific antibiotic usage patterns, and hospital environment dynamics. These insights suggest that early detection of at-risk patients can inform interventions such as preventive isolation and customized treatments, significantly improving clinical outcomes. The proposed GRU model for temporal classification achieved an average Receiver Operating Characteristic Area Under the Curve of 78.27 +- 1.26 over time, indicating strong predictive performance. In summary, this study highlights the clinical utility of our methodology, which combines predictive accuracy with interpretability, thereby facilitating more effective healthcare interventions by professionals.
This paper explores the application of large language models (LLMs) in nursing and elderly care, focusing on AI-driven patient monitoring and interaction. We introduce a novel Chinese nursing dataset and implement incremental pre-training (IPT) and supervised fine-tuning (SFT) techniques to enhance LLM performance in specialized tasks. Using LangChain, we develop a dynamic nursing assistant capable of real-time care and personalized interventions. Experimental results demonstrate significant improvements, paving the way for AI-driven solutions to meet the growing demands of healthcare in aging populations.