{"results":[{"id":"ss_ee16a5a270df2067827c1e25174894386e124bad","title":"Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example*","authors":[{"name":"P. Thoral"},{"name":"Jan Peppink"},{"name":"Ronald H. Driessen"},{"name":"E. Sijbrands"},{"name":"E. Kompanje"},{"name":"L. Kaplan"},{"name":"H. Bailey"},{"name":"J. Kesecioglu"},{"name":"M. Cecconi"},{"name":"M. Churpek"},{"name":"G. Clermont"},{"name":"M. van der Schaar"},{"name":"A. Ercole"},{"name":"A. Girbes"},{"name":"P. Elbers"}],"abstract":"Supplemental Digital Content is available in the text. OBJECTIVES: Critical care medicine is a natural environment for machine learning approaches to improve outcomes for critically ill patients as admissions to ICUs generate vast amounts of data. However, technical, legal, ethical, and privacy concerns have so far limited the critical care medicine community from making these data readily available. The Society of Critical Care Medicine and the European Society of Intensive Care Medicine have identified ICU patient data sharing as one of the priorities under their Joint Data Science Collaboration. To encourage ICUs worldwide to share their patient data responsibly, we now describe the development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, as an example of the feasibility of sharing complex critical care data. SETTING: University hospital ICU. SUBJECTS: Data from ICU patients admitted between 2003 and 2016. INTERVENTIONS: We used a risk-based deidentification strategy to maintain data utility while preserving privacy. In addition, we implemented contractual and governance processes, and a communication strategy. Patient organizations, supporting hospitals, and experts on ethics and privacy audited these processes and the database. MEASUREMENTS AND MAIN RESULTS: AmsterdamUMCdb contains approximately 1 billion clinical data points from 23,106 admissions of 20,109 patients. The privacy audit concluded that reidentification is not reasonably likely, and AmsterdamUMCdb can therefore be considered as anonymous information, both in the context of the U.S. Health Insurance Portability and Accountability Act and the European General Data Protection Regulation. The ethics audit concluded that responsible data sharing imposes minimal burden, whereas the potential benefit is tremendous. CONCLUSIONS: Technical, legal, ethical, and privacy challenges related to responsible data sharing can be addressed using a multidisciplinary approach. A risk-based deidentification strategy, that complies with both U.S. and European privacy regulations, should be the preferred approach to releasing ICU patient data. This supports the shared Society of Critical Care Medicine and European Society of Intensive Care Medicine vision to improve critical care outcomes through scientific inquiry of vast and combined ICU datasets.","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Medicine"],"doi":"10.1097/CCM.0000000000004916","url":"https://www.semanticscholar.org/paper/ee16a5a270df2067827c1e25174894386e124bad","pdf_url":"https://journals.lww.com/ccmjournal/Fulltext/2021/06000/Sharing_ICU_Patient_Data_Responsibly_Under_the.16.aspx","is_open_access":true,"citations":179,"published_at":"","score":70.37},{"id":"crossref_10.1007/s00134-026-08322-8","title":"Critical care in hematologic emergencies","authors":[{"name":"Pedro Castro"},{"name":"Colleen McEvoy"},{"name":"Elie Azoulay"}],"abstract":"","source":"CrossRef","year":2026,"language":"en","subjects":null,"doi":"10.1007/s00134-026-08322-8","url":"https://doi.org/10.1007/s00134-026-08322-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00134-026-08322-8.pdf","is_open_access":true,"published_at":"","score":70},{"id":"doaj_10.1016/j.jacadv.2026.102602","title":"Reply","authors":[{"name":"Justin Ren, PhD"},{"name":"Colin Royse, MBBS, MD"},{"name":"David H. Tian, MD, PhD"},{"name":"Nilesh Srivastav, MBBS"},{"name":"Alistair Royse, MBBS, MD"}],"abstract":"","source":"DOAJ","year":2026,"language":"","subjects":["Diseases of the circulatory (Cardiovascular) system","Medical emergencies. Critical care. Intensive care. First aid"],"doi":"10.1016/j.jacadv.2026.102602","url":"http://www.sciencedirect.com/science/article/pii/S2772963X26000190","is_open_access":true,"published_at":"","score":70},{"id":"arxiv_2604.00643","title":"In the Middle, Not on Top: AI-Mediated Communication for Patient-Provider Care Relationships","authors":[{"name":"Ut Gong"},{"name":"Yibo Meng"},{"name":"Qihan Zhang"},{"name":"Xin Chen"},{"name":"Yan Guan"}],"abstract":"Relationship-centered care relies on trust and meaningful connection. As AI enters clinical settings, we must ask not just what it can do, but how it should be positioned to support these values. We examine a \"middle, not top\" approach where AI mediates communication without usurping human judgment. Through studies of CLEAR, an asynchronous messaging system, we show how this configuration addresses real-world constraints like time pressure and uneven health literacy. We find that mediator affordances (e.g., availability, neutrality) redistribute interpretive work and reduce relational friction. Ultimately, we frame AI mediation as relational infrastructure, highlighting critical design tensions around framing power and privacy.","source":"arXiv","year":2026,"language":"en","subjects":["cs.HC"],"url":"https://arxiv.org/abs/2604.00643","pdf_url":"https://arxiv.org/pdf/2604.00643","is_open_access":true,"published_at":"2026-04-01T08:53:40Z","score":70},{"id":"arxiv_2603.08589","title":"CARE-Edit: Condition-Aware Routing of Experts for Contextual Image Editing","authors":[{"name":"Yucheng Wang"},{"name":"Zedong Wang"},{"name":"Yuetong Wu"},{"name":"Yue Ma"},{"name":"Dan Xu"}],"abstract":"Unified diffusion editors often rely on a fixed, shared backbone for diverse tasks, suffering from task interference and poor adaptation to heterogeneous demands (e.g., local vs global, semantic vs photometric). In particular, prevalent ControlNet and OmniControl variants combine multiple conditioning signals (e.g., text, mask, reference) via static concatenation or additive adapters which cannot dynamically prioritize or suppress conflicting modalities, thus resulting in artifacts like color bleeding across mask boundaries, identity or style drift, and unpredictable behavior under multi-condition inputs. To address this, we propose Condition-Aware Routing of Experts (CARE-Edit) that aligns model computation with specific editing competencies. At its core, a lightweight latent-attention router assigns encoded diffusion tokens to four specialized experts--Text, Mask, Reference, and Base--based on multi-modal conditions and diffusion timesteps: (i) a Mask Repaint module first refines coarse user-defined masks for precise spatial guidance; (ii) the router applies sparse top-K selection to dynamically allocate computation to the most relevant experts; (iii) a Latent Mixture module subsequently fuses expert outputs, coherently integrating semantic, spatial, and stylistic information to the base images. Experiments validate CARE-Edit's strong performance on contextual editing tasks, including erasure, replacement, text-driven edits, and style transfer. Empirical analysis further reveals task-specific behavior of specialized experts, showcasing the importance of dynamic, condition-aware processing to mitigate multi-condition conflicts.","source":"arXiv","year":2026,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2603.08589","pdf_url":"https://arxiv.org/pdf/2603.08589","is_open_access":true,"published_at":"2026-03-09T16:40:47Z","score":70},{"id":"ss_1cb11e88d1fcc20a85a1e585c9b13fa5e6fe72bf","title":"Nutrition in the intensive care unit: from the acute phase to beyond","authors":[{"name":"A. D. de Man"},{"name":"J. Gunst"},{"name":"A. Reintam Blaser"}],"abstract":"Recent randomized controlled trials (RCTs) have shown no benefit but dose-dependent harm by early full nutritional support in critically ill patients. Lack of benefit may be explained by anabolic resistance, suppression of cellular repair processes, and aggravation of hyperglycemia and insulin needs. Also early high amino acid doses did not provide benefit, but instead associated with harm in patients with organ dysfunctions. However, most studies focused on nutritional interventions initiated during the first days after intensive care unit admission. Although the intervention window of some RCTs extended into the post-acute phase of critical illness, no large RCTs studied nutritional interventions initiated beyond the first week. Hence, clear evidence-based guidance on when and how to initiate and advance nutrition is lacking. Prolonged underfeeding will come at a price as there is no validated metabolic monitor that indicates readiness for medical nutrition therapy, and an adequate response to nutrition, which likely varies between patients. Also micronutrient status cannot be assessed reliably, as inflammation can cause redistribution, so that plasma micronutrient concentrations are not necessarily reflective of total body stores. Moreover, high doses of individual micronutrients have not proven beneficial. Accordingly, current evidence provides clear guidance on which nutritional strategies to avoid, but the ideal nutritional regimen for individual patients remains unclear. In this narrative review, we summarize the findings of recent studies, discuss possible mechanisms explaining the results, point out pitfalls in interpretation of RCTs and their effect on clinical practice, and formulate suggestions for future research.","source":"Semantic Scholar","year":2024,"language":"en","subjects":["Medicine"],"doi":"10.1007/s00134-024-07458-9","url":"https://www.semanticscholar.org/paper/1cb11e88d1fcc20a85a1e585c9b13fa5e6fe72bf","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00134-024-07458-9.pdf","is_open_access":true,"citations":50,"published_at":"","score":69.5},{"id":"ss_c9165d066282cb67dfb41b9db99a7589c92227df","title":"Development and Validation of a Dynamic Real-Time Risk Prediction Model for Intensive Care Units Patients Based on Longitudinal Irregular Data: Multicenter Retrospective Study","authors":[{"name":"Zhuo Zheng"},{"name":"Jiawei Luo"},{"name":"Yingchao Zhu"},{"name":"L. Du"},{"name":"Lan Lan"},{"name":"Xiaobo Zhou"},{"name":"Xiaoyan Yang"},{"name":"Shixin Huang"}],"abstract":"Background Timely and accurate prediction of short-term mortality is critical in intensive care units (ICUs), where patients’ conditions change rapidly. Traditional scoring systems, such as the Simplified Acute Physiology Score and Acute Physiology and Chronic Health Evaluation, rely on static variables collected within the first 24 hours of admission and do not account for continuously evolving clinical states. These systems lack real-time adaptability, interpretability, and generalizability. With the increasing availability of high-frequency electronic medical record (EMR) data, machine learning (ML) approaches have emerged as powerful tools to model complex temporal patterns and support dynamic clinical decision-making. However, existing models are often limited by their inability to handle irregular sampling and missing values, and many lack rigorous external validation across institutions. Objective We aimed to develop a real-time, interpretable risk prediction model that continuously assesses ICU patient mortality using irregular, longitudinal EMR data, with improved performance and generalizability over traditional static scoring systems. Methods A time-aware bidirectional attention-based long short-term memory (TBAL) model was developed using EMR data from the MIMIC-IV (Medical Information Mart for Intensive Care) and eICU Collaborative Research Database (eICU-CRD) databases, comprising 176,344 ICU stays. The model incorporated dynamic variables, including vital signs, laboratory results, and medication data, updated hourly, to perform static and continuous mortality risk assessments. External cross-validation and subgroup sensitivity analyses were conducted to evaluate robustness and fairness. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, and F1-score. Interpretability was enhanced using integrated gradients to identify key predictors. Results For the static 12-hour to 1-day mortality prediction task, the TBAL model achieved AUROCs of 95.9 (95% CI 94.2-97.5) and 93.3 (95% CI 91.5-95.3) and AUPRCs of 48.5 and 21.6 in MIMIC-IV and eICU-CRD, respectively. Accuracy and F1-scores reached 94.1 and 46.7 in MIMIC-IV and 92.2 and 28.1 in eICU-CRD. In dynamic prediction tasks, AUROCs reached 93.6 (95% CI 93.2-93.9) and 91.9 (95% CI 91.6-92.1), with AUPRCs of 41.3 and 50, respectively. The model maintained high recall for positive cases (82.6% and 79.1% in MIMIC-IV and eICU-CRD). Cross-database validation yielded AUROCs of 81.3 and 76.1, confirming generalizability. Subgroup analysis showed stable performance across age, sex, and severity strata, with top predictors including lactate, vasopressor use, and Glasgow Coma Scale score. Conclusions The TBAL model offers a robust, interpretable, and generalizable solution for dynamic real-time mortality risk prediction in ICU patients. Its ability to adapt to irregular temporal patterns and to provide hourly updated predictions positions it as a promising decision-support tool. Future work should validate its utility in prospective clinical trials and investigate its integration into real-world ICU workflows to enhance patient outcomes.","source":"Semantic Scholar","year":2025,"language":"en","subjects":["Medicine"],"doi":"10.2196/69293","url":"https://www.semanticscholar.org/paper/c9165d066282cb67dfb41b9db99a7589c92227df","is_open_access":true,"citations":7,"published_at":"","score":69.21000000000001},{"id":"crossref_10.1016/j.iccn.2025.104103","title":"In-flight medical emergencies: Transdisciplinary collaboration at 30,000 feet","authors":[{"name":"Margo Hoyler"},{"name":"Ashish K. Khanna"}],"abstract":"","source":"CrossRef","year":2025,"language":"en","subjects":null,"doi":"10.1016/j.iccn.2025.104103","url":"https://doi.org/10.1016/j.iccn.2025.104103","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.5811/cpcem.24999","title":"Use of Point-of-care Ultrasound for Placement of a Gastric Tamponade Balloon","authors":[{"name":"Patrick Minges"},{"name":"Martina Diaz McDermott"},{"name":"Jazmyn Shaw"}],"abstract":"Case Presentation: A 30-year-old female with a history of alcoholic cirrhosis and esophageal varices presented with massive hematemesis. A gastric balloon tamponade device was subsequently placed to temporize variceal hemorrhage, and point-of-care ultrasound (POCUS) was used to confirm the appropriate placement of the gastric balloon before complete inflation. We describe a novel use of ultrasound for use in severely ill patients with gastrointestinal (GI) bleeding. Discussion: A fluid-filled and distended stomach has long been recognized as a cause of a false-positive focused assessment with sonography in trauma exam but may also be a vital piece of information in the scenario of a patient with suspected upper GI hemorrhage. There is very little description in the literature of using POCUS to confirm the appropriate placement of a gastric tamponade balloon with none by emergency physicians.. Ultrasound may offer advantages over plain radiography in this application given its speed and safety; thus, its utility for this task is worth further investigation.","source":"DOAJ","year":2025,"language":"","subjects":["Medical emergencies. Critical care. Intensive care. First aid"],"doi":"10.5811/cpcem.24999","url":"https://escholarship.org/uc/item/0zt8b4vv","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.5114/ait/209457","title":"The consensus statement of the Section of Paediatric Anaesthesiology and Intensive Therapy of the Polish Society of Anaesthesiology and Intensive Therapy on anaesthesia in children under 3 years of age","authors":[{"name":"Marzena Zielińska"},{"name":"Alicja Bartkowska-Śniatkowska"},{"name":"Magdalena Mierzewska-Schmidt"},{"name":"Jowita Biernawska"},{"name":"Elżbieta Byrska-Maciejak"},{"name":"Maciej Cettler"},{"name":"Magdalena Chęcińska"},{"name":"Maria Damps"},{"name":"Anna Kubica-Cielińska"},{"name":"Małgorzata Mikaszewska-Sokolewicz"},{"name":"Jowita Rosada-Kurasińska"},{"name":"Beata Rybojad"},{"name":"Tomasz Sikorski"},{"name":"Magdalena Świder"},{"name":"Mariola Tałałaj"},{"name":"Izabela Pągowska-Klimek"}],"abstract":"The anaesthesia of a young child under 3 years of age is a challenge for every anaesthetist. The peculiarities of this group of patients, particularly neonates and infants, resulting primarily from differences in both physiology, anatomy and the immaturity of individual organs which translate into different pharmacokinetics and pharmacodynamics of the drugs used in anaesthesiology, underlie the significantly more frequently recorded critical events during anaesthesia compared with the adult patient population.\nConcerned about the safety of children undergoing anaesthesia and aiming to ensure the highest possible quality and uniform standard of anaesthetic services, the Expert Panel of the Section of Paediatric Anaesthesiology and Intensive Care has prepared a Section position paper on anaesthesia in children under 3 years of age.","source":"DOAJ","year":2025,"language":"","subjects":["Anesthesiology","Medical emergencies. Critical care. Intensive care. First aid"],"doi":"10.5114/ait/209457","url":"https://www.ait-journal.com/The-consensus-statement-of-the-Section-of-Paediatric-Anaesthesiology-and-Intensive,209457,0,2.html","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.35401/2541-9897-2025-10-1-101-109","title":"Protective Potential of Sodium-Glucose Cotransporter 2 Inhibitors in Internal Medicine (Part 2)","authors":[{"name":"Ashot A. Avagimyan"},{"name":"Mohammad Sheibani"},{"name":"Artem I. Trofimenko"},{"name":"Evgenii E. Lysov"},{"name":"Farida M. Khamidova"},{"name":"Anahit Z. Aznauryan"},{"name":"Lilit M. Sukiasyan"},{"name":"Karmen T. Sahakyan"},{"name":"Tamara R. Gevorgyan"},{"name":"Marina R. Tatoyan"},{"name":"Gayane L. Mkrtchyan"},{"name":"Goharik L. Meltonyan"},{"name":"Anna R. Petrosyan"},{"name":"Ludmila A. Martemyanova"},{"name":"Ruzanna R. Petrosyan"},{"name":"Olga I. Urazova"},{"name":"Nana V. Pogosova"},{"name":"Nizal Sarrafzadegan"}],"abstract":"Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are now uncovering new possibilities in the field of internal medicine owing to their diverse protective effects. In the second part of the literature review, we explore potential applications of SGLT2i in hepatology, neurology, ophthalmology, and oncology, mechanisms of action of such drugs as dapagliflozin, empagliflozin, canagliflozin, etc, and their effect on different organs and systems.","source":"DOAJ","year":2025,"language":"","subjects":["Neoplasms. Tumors. Oncology. Including cancer and carcinogens","Diseases of the circulatory (Cardiovascular) system","Surgery","Medical emergencies. Critical care. Intensive care. First aid"],"doi":"10.35401/2541-9897-2025-10-1-101-109","url":"https://www.innovmedkub.ru/jour/article/view/1072","is_open_access":true,"published_at":"","score":69},{"id":"arxiv_2506.16141","title":"GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning","authors":[{"name":"Yi Chen"},{"name":"Yuying Ge"},{"name":"Rui Wang"},{"name":"Yixiao Ge"},{"name":"Junhao Cheng"},{"name":"Ying Shan"},{"name":"Xihui Liu"}],"abstract":"Recent reinforcement learning approaches, such as outcome-supervised GRPO, have advanced Chain-of-Thought reasoning in large language models (LLMs), yet their adaptation to multimodal LLMs (MLLMs) is unexplored. To address the lack of rigorous evaluation for MLLM post-training methods, we introduce SEED-Bench-R1, a benchmark with complex real-world videos requiring balanced perception and reasoning. It offers a large training set and evaluates generalization across three escalating challenges: in-distribution, cross-environment, and cross-environment-task scenarios. Using SEED-Bench-R1, we find that standard GRPO, while improving answer accuracy, often reduces logical coherence between reasoning steps and answers, with only a 57.9% consistency rate. This stems from reward signals focusing solely on final answers, encouraging shortcuts, and strict KL penalties limiting exploration.To address this, we propose GRPO-CARE, a consistency-aware RL framework optimizing both answer correctness and reasoning coherence without explicit supervision. GRPO-CARE introduces a two-tiered reward: (1) a base reward for answer correctness, and (2) an adaptive consistency bonus, computed by comparing the model's reasoning-to-answer likelihood (via a slowly-evolving reference model) against group peers.This dual mechanism amplifies rewards for reasoning paths that are both correct and logically consistent. Replacing KL penalties with this adaptive bonus, GRPO-CARE outperforms standard GRPO on SEED-Bench-R1, achieving a 6.7% performance gain on the hardest evaluation level and a 24.5% improvement in consistency. It also shows strong transferability, improving model performance across diverse video understanding benchmarks. Our work contributes a systematically designed benchmark and a generalizable post-training framework, advancing the development of more interpretable and robust MLLMs.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CV","cs.AI","cs.CL","cs.LG"],"url":"https://arxiv.org/abs/2506.16141","pdf_url":"https://arxiv.org/pdf/2506.16141","is_open_access":true,"published_at":"2025-06-19T08:49:13Z","score":69},{"id":"ss_790a5f0072d08fd7a133703f569be138e73ce5be","title":"Artificial intelligence to advance acute and intensive care medicine","authors":[{"name":"L. Biesheuvel"},{"name":"D. Dongelmans"},{"name":"P. Elbers"}],"abstract":"Purpose of review This review explores recent key advancements in artificial intelligence for acute and intensive care medicine. As artificial intelligence rapidly evolves, this review aims to elucidate its current applications, future possibilities, and the vital challenges that are associated with its integration into emergency medical dispatch, triage, medical consultation and ICUs. Recent findings The integration of artificial intelligence in emergency medical dispatch (EMD) facilitates swift and accurate assessment. In the emergency department (ED), artificial intelligence driven triage models leverage diverse patient data for improved outcome predictions, surpassing human performance in retrospective studies. Artificial intelligence can streamline medical documentation in the ED and enhances medical imaging interpretation. The introduction of large multimodal generative models showcases the future potential to process varied biomedical data for comprehensive decision support. In the ICU, artificial intelligence applications range from early warning systems to treatment suggestions. Summary Despite promising academic strides, widespread artificial intelligence adoption in acute and critical care is hindered by ethical, legal, technical, organizational, and validation challenges. Despite these obstacles, artificial intelligence's potential to streamline clinical workflows is evident. When these barriers are overcome, future advancements in artificial intelligence have the potential to transform the landscape of patient care for acute and intensive care medicine.","source":"Semantic Scholar","year":2024,"language":"en","subjects":["Medicine"],"doi":"10.1097/MCC.0000000000001150","url":"https://www.semanticscholar.org/paper/790a5f0072d08fd7a133703f569be138e73ce5be","pdf_url":"https://journals.lww.com/co-criticalcare/fulltext/9900/artificial_intelligence_to_advance_acute_and.162.aspx","is_open_access":true,"citations":24,"published_at":"","score":68.72},{"id":"ss_9e20fd5871a1442c6b700d994e0ddd9586ed195f","title":"Healthcare professionals perspective of the facilitators and barriers to family engagement during patient-and-family-centered-care interdisciplinary rounds in intensive care unit: A qualitative exploratory study.","authors":[{"name":"Brigitte S. Cypress"},{"name":"Rida Gharzeddine"},{"name":"Mei Rosemary Fu"},{"name":"Melanie Ransom"},{"name":"Farley A Villarente"},{"name":"Caitlyn Pitman"}],"abstract":"OBJECTIVES Family engagement in care for critically ill patients remains an inconsistent practice and an understudied area of nursing science. Rounds for this study is an interdisciplinary activity conducted at the bedside in partnership with patients, their families, and the health care professionals involved in providing the care. We sought to explore and describe the facilitators and barriers to family engagement during patient and family-centered interdisciplinary rounds in the intensive care unit. RESEARCH METHODOLOGY/DESIGN This qualitative exploratory study is part of a multisite experimental study (#Pro2020001614; NCT05449990). We analyzed the narrative data from the qualitative questions added in the survey from 52 healthcare professionals involved in a multisite experimental study using Braun and Clarke's (2006) constructionist, contextualist approach to thematic analysis. SETTING The study was conducted in the intensive care unit of two medical centers. MAIN OUTCOME MEASURES The findings presented are themes illuminated from thematic analysis namely communication gaps, family's lack of resources, familial and healthcare providers' characteristics, lack of leadership, interprofessional support, policy, and guidelines. FINDINGS Family engagement in critical care during interdisciplinary rounds occurred within the intersectionality among families, healthcare professionals' practice, and organizational factors. The facilitators for family engagement include supported, championed, and advocated-for family adaptation, teams, and professional practice, and organizational receptivity, and support. Communication and leadership are the precursors to family engagement. CONCLUSIONS The findings added new knowledge for exploring the nature and scope of family engagement in critical care. Family engagement must be incorporated into the organizational vision and mission, and healthcare delivery systems. IMPLICATIONS FOR CLINICAL PRACTICE There is a need to further investigate the resources, organizational support mechanisms, and systems that affect patients, families, and healthcare professionals, and the establishment of policies that will aid in reducing barriers to family engagement in the intensive care unit.","source":"Semantic Scholar","year":2024,"language":"en","subjects":["Medicine"],"doi":"10.1016/j.iccn.2024.103636","url":"https://www.semanticscholar.org/paper/9e20fd5871a1442c6b700d994e0ddd9586ed195f","is_open_access":true,"citations":18,"published_at":"","score":68.53999999999999},{"id":"ss_be49cbe5df3a18106becae3453c252838aabeb25","title":"Early physical rehabilitation dosage in the intensive care unit associates with hospital outcomes after critical COVID-19","authors":[{"name":"K. Mayer"},{"name":"Evan Haezebrouck"},{"name":"Lori M. Ginoza"},{"name":"Clarisa Martinez"},{"name":"Minnie Jan"},{"name":"Lori A. Michener"},{"name":"L. Fresenko"},{"name":"A. Montgomery-Yates"},{"name":"A. Kalema"},{"name":"A. Pastva"},{"name":"Michelle Biehl"},{"name":"Matthew F. Mart"},{"name":"Joshua K. Johnson"}],"abstract":"To examine the relationship between physical rehabilitation parameters including an approach to quantifying dosage with hospital outcomes for patients with critical COVID-19. Retrospective practice analysis from March 5, 2020, to April 15, 2021. Intensive care units (ICU) at four medical institutions. n = 3780 adults with ICU admission and diagnosis of COVID-19. We measured the physical rehabilitation treatment delivered in ICU and patient outcomes: (1) mortality; (2) discharge disposition; and (3) physical function at hospital discharge measured by the Activity Measure-Post Acute Care (AM-PAC) “6-Clicks” (6–24, 24 = greater functional independence). Physical rehabilitation dosage was defined as the average mobility level scores in the first three sessions (a surrogate measure of intensity) multiplied by the rehabilitation frequency (PT + OT frequency in hospital). The cohort was a mean 64 ± 16 years old, 41% female, mean BMI of 32 ± 9 kg/m2 and 46% (n = 1739) required mechanical ventilation. For 2191 patients who received rehabilitation, the dosage and AM-PAC at discharge were moderately, positively associated (Spearman’s rho [r] = 0.484, p \u003c 0.001). Multivariate linear regression (model adjusted R2 = 0.68, p \u003c 0.001) demonstrates mechanical ventilation (β = − 0.86, p = 0.001), average mobility score in first three sessions (β = 2.6, p \u003c 0.001) and physical rehabilitation dosage (β = 0.22, p = 0.001) were predictive of AM-PAC scores at discharge when controlling for age, sex, BMI, and ICU LOS. Greater physical rehabilitation exposure early in the ICU is associated with better physical function at hospital discharge.","source":"Semantic Scholar","year":2024,"language":"en","subjects":["Medicine"],"doi":"10.1186/s13054-024-05035-6","url":"https://www.semanticscholar.org/paper/be49cbe5df3a18106becae3453c252838aabeb25","pdf_url":"https://doi.org/10.1186/s13054-024-05035-6","is_open_access":true,"citations":12,"published_at":"","score":68.36},{"id":"ss_5f4b80fbf8eb37e86b88d929166a36023ff1f8ea","title":"The Caregiver Pathway Intervention Can Contribute to Reduced Post-Intensive Care Syndrome Among Family Caregivers of ICU Survivors: A Randomized Controlled Trial","authors":[{"name":"Solbjørg Watland"},{"name":"Lise Solberg Nes"},{"name":"Ø. Ekeberg"},{"name":"Morten Rostrup"},{"name":"Elizabeth Hanson"},{"name":"Mirjam Ekstedt"},{"name":"Una Stenberg"},{"name":"Milada Hagen"},{"name":"Elin Børøsund"}],"abstract":"OBJECTIVES: Explore short-term effects of “The Caregiver Pathway,” an intervention for family caregiver follow-up, on Post-Intensive Care Syndrome symptoms among families (PICS-F). DESIGN: A randomized controlled trial. SETTING: A medical ICU at a Norwegian University Hospital. PARTICIPANTS: One hundred ninety-six family caregivers of critically ill patients randomized to an intervention (n = 101) or control group (n = 95). INTERVENTIONS: “The Caregiver Pathway” four-step model offers individual and structured follow-up, including: 1) mapping family caregivers’ needs and concerns with an assessment tool followed by a conversation with an ICU nurse within the first days at the ICU, 2) a supportive card when leaving the ICU, 3) offer for the family caregivers to receive a phone call after ICU patient discharge, and 4) a follow-up conversation within 3 months. MEASUREMENTS AND MAIN RESULTS: Data were collected at baseline and after 3 months and analyzed using linear regression. No significant effects were detected when comparing all participants completing 3-month outcome measurements (n = 144). A subgroups analysis stratified on patient survival, however, showed statistically significant effect for family caregivers of patients surviving the ICU stay receiving the intervention compared with controls. Caregivers of surviving patients reported improved symptoms related to post-traumatic stress disorder, measured by Impact of Event Scale-Revised (B = –8.2 [95% CI, –14.2 to –2.2]; p = 0.008), anxiety (B = –2.2 [95% CI, –4.0 to –0.5]; p = 0.014), and depression (B = –1.5 [95% CI, –2.9 to –0.1]; p = 0.035); measured by the Hospital Anxiety and Depression Scale, subscore physical functioning in health-related quality of life (B = 9.7 [95% CI, 0.3–19.0]; p = 0.043); measured by Short Form 12-Item Health Survey; and hope (B = 2.4 [95% CI, 0.4–4.3]; p = 0.017) and measured by the Herth Hope Index. At 3-month, the model did not appear to improve the outcomes for family caregivers of nonsurviving patients. CONCLUSIONS: “The Caregiver Pathway” intervention was associated with reduced symptoms of PICS-F in family caregivers of surviving ICU patients compared with controls.","source":"Semantic Scholar","year":2024,"language":"en","subjects":["Medicine"],"doi":"10.1097/CCM.0000000000006546","url":"https://www.semanticscholar.org/paper/5f4b80fbf8eb37e86b88d929166a36023ff1f8ea","is_open_access":true,"citations":9,"published_at":"","score":68.27000000000001},{"id":"ss_755c73ad6804a199f285919ca541712fcacc7058","title":"Optimizing processes of care and time to diagnosis in acute aortic dissection patients in a chest pain center by implementing a multidisciplinary cooperative first aid mode - A quality improvement report.","authors":[{"name":"Wenyan Xia"},{"name":"Xiaohui Shi"},{"name":"Qian Chen"},{"name":"Rong Yang"}],"abstract":"OBJECTIVE This study aimed to explore the effectiveness of a multidisciplinary cooperative first aid model in the process of establishing a chest pain center specializing in acute aortic dissection (AD). DESIGN A quality improvement report. METHODS A total of 142 patients with acute aortic dissection treated before and after the optimization of the chest pain center process in our hospital were included. According to their admission time: the group before the optimization process was designated as the control group (66 cases) and the group after the optimization process was the intervention group (76 cases). The control group received conventional emergency treatment, while the intervention group received treatment through a multidisciplinary cooperative first aid model. The treatment times for both groups were compared: the time from first medical contact(FMC) to completion of an electrocardiogram (ECG), the diagnosis time, and the time spent in the emergency department. RESULTS The research findings revealed that the intervention group had significantly shorter times for FMC-to-ECG, diagnosis time, and emergency stay compared to the control group (P \u003c 0.001). CONCLUSION Our findings indicate that by optimizing the multidisciplinary cooperative first aid model and procedures, the treatment of patients has indeed been effectively ensured, achieving safety outcomes. IMPLICATIONS FOR CLINICAL PRACTICE For chest pain centers, we suggest that to use multidisciplinary cooperative first aid model to get repaid and definite diagnosis of various causes of chest pain. A bedside transthoracic echocardiography is recommended to use in order to identify AD before proceeding with further treatment.","source":"Semantic Scholar","year":2024,"language":"en","subjects":["Medicine"],"doi":"10.1016/j.iccn.2024.103765","url":"https://www.semanticscholar.org/paper/755c73ad6804a199f285919ca541712fcacc7058","pdf_url":"https://doi.org/10.1016/j.iccn.2024.103765","is_open_access":true,"citations":1,"published_at":"","score":68.03},{"id":"crossref_10.1016/j.iccn.2024.103780","title":"Non-medical prescribing in critical care","authors":[{"name":"Thomas Holgate"}],"abstract":"","source":"CrossRef","year":2024,"language":"en","subjects":null,"doi":"10.1016/j.iccn.2024.103780","url":"https://doi.org/10.1016/j.iccn.2024.103780","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.1002/hkj2.12048","title":"Clinical presentations and dispositions of transient ischemic attack and minor stroke patients at the emergency department of a tertiary hospital in southern Thailand: A retrospective study","authors":[{"name":"Tanawin Sakarin"},{"name":"Tippawan Liabsuetrakul"}],"abstract":"Abstract Objective To assess the dispositions, management, and clinical outcomes of TIAMS patients in ED to improve the quality of management in ED. Material and Method A descriptive retrospective study was conducted in ED patients aged \u003e18 years diagnosed with TIAMS in the ED from 1 January 2018, to 31 January 2019. Data regarding terms of clinical presentation, examination, management, disposition, and adverse events were collected. Results Three hundred and sixty‐three TIAMS patients were enrolled in the study. Majority of the patients aged \u003c45 years were admitted or referred (15.4%). The highest proportion of patients whose onset times from the last normal were less than 4.5 h were admitted to the EDOU (55.6%), while all patients whose onset times from the last normal were more than 48 h were discharged. Patients with abnormal cerebellar signs or atrial fibrillation were less likely to be discharged from the hospital. Patients with lower National Institutes of Health Stroke Scale (NIHSS) and ABCD2 scores tended to be discharged. Conclusion Among TIAMS patients, age, symptom onset, presence of atrial fibrillation, positive cerebellar signs, and severity scores influenced the disposition. There was no difference in adverse events among disposition groups.","source":"DOAJ","year":2024,"language":"","subjects":["Surgery","Medical emergencies. Critical care. Intensive care. First aid"],"doi":"10.1002/hkj2.12048","url":"https://doi.org/10.1002/hkj2.12048","is_open_access":true,"published_at":"","score":68},{"id":"arxiv_2411.16170","title":"CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction","authors":[{"name":"Yuan Zhou"},{"name":"Qingshan Xu"},{"name":"Jiequan Cui"},{"name":"Junbao Zhou"},{"name":"Jing Zhang"},{"name":"Richang Hong"},{"name":"Hanwang Zhang"}],"abstract":"Recently, large efforts have been made to design efficient linear-complexity visual Transformers. However, current linear attention models are generally unsuitable to be deployed in resource-constrained mobile devices, due to suffering from either few efficiency gains or significant accuracy drops. In this paper, we propose a new de\\textbf{C}oupled du\\textbf{A}l-interactive linea\\textbf{R} att\\textbf{E}ntion (CARE) mechanism, revealing that features' decoupling and interaction can fully unleash the power of linear attention. We first propose an asymmetrical feature decoupling strategy that asymmetrically decouples the learning process for local inductive bias and long-range dependencies, thereby preserving sufficient local and global information while effectively enhancing the efficiency of models. Then, a dynamic memory unit is employed to maintain critical information along the network pipeline. Moreover, we design a dual interaction module to effectively facilitate interaction between local inductive bias and long-range information as well as among features at different layers. By adopting a decoupled learning way and fully exploiting complementarity across features, our method can achieve both high efficiency and accuracy. Extensive experiments on ImageNet-1K, COCO, and ADE20K datasets demonstrate the effectiveness of our approach, e.g., achieving $78.4/82.1\\%$ top-1 accuracy on ImagegNet-1K at the cost of only $0.7/1.9$ GMACs. Codes will be released on \\href{https://github.com/zhouyuan888888/CARE-Transformer}{https://github.com/zhouyuan888888/CARE-Transformer}.","source":"arXiv","year":2024,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2411.16170","pdf_url":"https://arxiv.org/pdf/2411.16170","is_open_access":true,"published_at":"2024-11-25T07:56:13Z","score":68}],"total":7503740,"page":1,"page_size":20,"sources":["CrossRef","DOAJ","arXiv","Semantic Scholar"],"query":"Medical emergencies. Critical care. Intensive care. First aid"}