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Narrowing the communication and knowledge gap between producers and users of scientific data is a longstanding problem in ecological conservation and land management. Decision support tools (DSTs), including websites or interactive web applications, provide platforms that can help bridge this gap. DSTs can most effectively disseminate and translate research results when producers and users collaboratively and iteratively design content and features. One data resource seldom incorporated into DSTs are species distribution models (SDMs), which can produce spatial predictions of habitat suitability. Outputs from SDMs can inform management decisions, but their complexity and inaccessibility can limit their use by resource managers or policy makers. To overcome these limitations, we present the Invasive Species Habitat Tool (INHABIT), a novel, web-based DST built with R Shiny to display spatial predictions and tabular summaries of habitat suitability from SDMs for invasive plants across the contiguous United States. INHABIT provides actionable science to support the prevention and management of invasive species. Two case studies demonstrate the important role of end user feedback in confirming INHABIT's credibility, utility, and relevance.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8824347 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0263056 | PLOS |
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Florida Orthopaedic Institute, Gainesville, FL, 32607, USA.
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View Article and Find Full Text PDFBMC Med Inform Decis Mak
September 2025
Emergency Department, Helios Spital, Überlingen, Germany.
Background: The increasing amount of data routinely collected on ICUs poses a challenge for clinicians which is aggravated with data-heavy therapies like Continuous Kidney Replacement Therapy (CKRT). We developed the CKRT Supporting Software Prototype (CKRT-SSP), a clinical decision support system for use before, during and after CKRT. The aim of this user experience (UX) study was to prospectively evaluate CKRT-SSP in terms of usability, user experience, and workload in a simulated ICU setting.
View Article and Find Full Text PDFJ Assist Reprod Genet
September 2025
Department of Gynecology, Pingxiang Maternal and Child Health Hospital, PingXiang, Jiangxi, China.
Objective: This study aimed to identify key predictors of uterine fibroid (UF) recurrence following laparoscopic myomectomy (LM) in reproductive-age women and to construct a predictive nomogram to support individualized clinical decision-making.
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Geroscience
September 2025
Department of Emergency and Internal Medicine, Skåne University Hospital, Malmö, Sweden.
To evaluate a simplified version of the Clinical Frailty Scale (SCFS) among older adults presenting to the emergency department (ED) with acute dyspnea. In this retrospective single-center cohort study, we included patients from the Acute Dyspnea Study (ADYS) cohort. Severity of illness was assessed using the Medical Emergency Triage and Treatment System (METTS).
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September 2025
Community Medicine Education Promotion Office, Faculty of Medicine, Kagawa University Ikenobe, 1750-1, Miki-Cho, Kagawa, 761-0793, Japan.
Generative artificial intelligence (AI) is rapidly transforming perioperative medicine, particularly anesthesiology, by enabling novel applications, such as real-time data synthesis, individualized risk prediction, and automated documentation. These capabilities enhance clinical decision-making, patient communication, and workflow efficiency in the operating room. In education, generative AI offers immersive simulations and tailored learning experiences that improve both technical skills and professional judgment.
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