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Search Results (680)

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Keywords = real-world clinical practice

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15 pages, 534 KiB  
Review
Evolving Treatment Paradigms in Metastatic Hormone-Sensitive Prostate Cancer: Expert Narrative Review
by Vineet Talwar, Kaushal Kalra, Akhil Kapoor, P. S. Dattatreya, Amit Joshi, Krishna Chaitanya, M. V. Chandrakanth, Atul Batra, Krishna Prasad, Nikhil Haridas and Nilesh Lokeshwar
Curr. Oncol. 2025, 32(8), 437; https://doi.org/10.3390/curroncol32080437 - 5 Aug 2025
Abstract
The treatment landscape of metastatic hormone-sensitive prostate cancer (mHSPC) has transformed significantly with the advent of triplet therapy involving androgen deprivation therapy (ADT), docetaxel, and androgen receptor signalling inhibitors (ARSIs). While clinical guidelines increasingly support early intensification, real-world practice remains challenged by patient [...] Read more.
The treatment landscape of metastatic hormone-sensitive prostate cancer (mHSPC) has transformed significantly with the advent of triplet therapy involving androgen deprivation therapy (ADT), docetaxel, and androgen receptor signalling inhibitors (ARSIs). While clinical guidelines increasingly support early intensification, real-world practice remains challenged by patient heterogeneity, evolving evidence, and limited consensus on treatment sequencing. This narrative review integrates evidence from landmark trials, clinical guidelines, and expert insights from oncologists managing mHSPC in India. Findings affirm that triplet therapy, particularly with darolutamide, improves survival in high-volume disease and underscores the need for personalized treatment based on disease burden, comorbidities, and genomic profiles. The review also highlights gaps in real-world data, sequencing strategies, and biomarker-driven therapy, reinforcing the need for precision medicine and locally relevant evidence to guide treatment. Ultimately, optimizing mHSPC management requires harmonizing guideline-based approaches with individualized, real-world decision making to improve patient outcomes. Full article
(This article belongs to the Section Genitourinary Oncology)
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12 pages, 535 KiB  
Article
Real-World Effectiveness of Rosuvastatin–Ezetimibe Single Pill (Rovazet®) in Korean Dyslipidemia Patients
by Hack-Lyoung Kim, Hyun Sung Joh, Sang-Hyun Kim and Myung-A Kim
J. Clin. Med. 2025, 14(15), 5480; https://doi.org/10.3390/jcm14155480 - 4 Aug 2025
Abstract
Background: Fixed-dose combinations of rosuvastatin and ezetimibe are increasingly used in clinical practice, but real-world data on their effectiveness and safety in large populations remain limited. Methods: This prospective, single-group, open-label, non-interventional observational study was conducted in the Republic of Korea to evaluate [...] Read more.
Background: Fixed-dose combinations of rosuvastatin and ezetimibe are increasingly used in clinical practice, but real-world data on their effectiveness and safety in large populations remain limited. Methods: This prospective, single-group, open-label, non-interventional observational study was conducted in the Republic of Korea to evaluate the effectiveness and safety of Rovazet® (a fixed-dose combination of rosuvastatin and ezetimibe). Patients were prospectively enrolled from 235 institutions (50 general hospitals and 185 private clinics) as part of routine clinical practice over a five-year period. Lipid profiles and medication compliance questionnaire results were collected at baseline, 12 weeks, and 24 weeks of treatment. Results: A total of 5527 patients with dyslipidemia, the majority were men (53.0%), and the mean age was 60.4 years. Rovazet® significantly reduced low-density lipoprotein cholesterol (LDL-C) by 23.5% at 12 weeks (from 117.47 ± 50.65 mg/dL to 81.14 ± 38.20 mg/dL; p < 0.0001) and by 27.4% at 24 weeks (from 117.47 ± 50.65 mg/dL to 74.52 ± 33.36 mg/dL; p < 0.0001). Total cholesterol was significantly reduced by 17.7% at 12 weeks and by 19.8% at 24 weeks. Rovazet® treatment reduced triglycerides by 4.1% at 12 weeks and by 7.2% at 24 weeks. High-density lipoprotein cholesterol increased by 4.5% at 12 weeks and by 7.9% at 24 weeks following Rovazet® treatment. These changes in lipid profiles were consistent, regardless of cardiovascular risk profiles. By 24 weeks of treatment with Rovazet®, 91.8% of patients had reached their target LDL-C goals. Adverse drug reactions were reported in 2.81% of patients, most of which were minor, indicating that Rovazet® was well tolerated. Conclusions: Rovazet® was effective in improving lipid profiles and well tolerated in Korean adults with dyslipidemia. Full article
(This article belongs to the Section Pharmacology)
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25 pages, 1751 KiB  
Review
Large Language Models for Adverse Drug Events: A Clinical Perspective
by Md Muntasir Zitu, Dwight Owen, Ashish Manne, Ping Wei and Lang Li
J. Clin. Med. 2025, 14(15), 5490; https://doi.org/10.3390/jcm14155490 - 4 Aug 2025
Abstract
Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained [...] Read more.
Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT) series, offer promising methods for automating ADE extraction from clinical data. These models have been applied to various aspects of pharmacovigilance and clinical decision support, demonstrating potential in extracting ADE-related information from real-world clinical data. Additionally, chatbot-assisted systems have been explored as tools in clinical management, aiding in medication adherence, patient engagement, and symptom monitoring. This narrative review synthesizes the current state of LLMs in ADE detection from a clinical perspective, organizing studies into categories such as human-facing decision support tools, immune-related ADE detection, cancer-related and non-cancer-related ADE surveillance, and personalized decision support systems. In total, 39 articles were included in this review. Across domains, LLM-driven methods have demonstrated promising performances, often outperforming traditional approaches. However, critical limitations persist, such as domain-specific variability in model performance, interpretability challenges, data quality and privacy concerns, and infrastructure requirements. By addressing these challenges, LLM-based ADE detection could enhance pharmacovigilance practices, improve patient safety outcomes, and optimize clinical workflows. Full article
(This article belongs to the Section Pharmacology)
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13 pages, 1520 KiB  
Article
Designing a Patient Outcome Clinical Assessment Tool for Modified Rankin Scale: “You Feel the Same Way Too”
by Laura London and Noreen Kamal
Informatics 2025, 12(3), 78; https://doi.org/10.3390/informatics12030078 (registering DOI) - 4 Aug 2025
Abstract
The modified Rankin Scale (mRS) is a widely used outcome measure for assessing disability in stroke care; however, its administration is often affected by subjectivity and variability, leading to poor inter-rater reliability and inconsistent scoring. Originally designed for hospital discharge evaluations, the mRS [...] Read more.
The modified Rankin Scale (mRS) is a widely used outcome measure for assessing disability in stroke care; however, its administration is often affected by subjectivity and variability, leading to poor inter-rater reliability and inconsistent scoring. Originally designed for hospital discharge evaluations, the mRS has evolved into an outcome tool for disability assessment and clinical decision-making. Inconsistencies persist due to a lack of standardization and cognitive biases during its use. This paper presents design principles for creating a standardized clinical assessment tool (CAT) for the mRS, grounded in human–computer interaction (HCI) and cognitive engineering principles. Design principles were informed in part by an anonymous online survey conducted with clinicians across Canada to gain insights into current administration practices, opinions, and challenges of the mRS. The proposed design principles aim to reduce cognitive load, improve inter-rater reliability, and streamline the administration process of the mRS. By focusing on usability and standardization, the design principles seek to enhance scoring consistency and improve the overall reliability of clinical outcomes in stroke care and research. Developing a standardized CAT for the mRS represents a significant step toward improving the accuracy and consistency of stroke disability assessments. Future work will focus on real-world validation with healthcare stakeholders and exploring self-completed mRS assessments to further refine the tool. Full article
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22 pages, 409 KiB  
Article
Employing Machine Learning and Deep Learning Models for Mental Illness Detection
by Yeyubei Zhang, Zhongyan Wang, Zhanyi Ding, Yexin Tian, Jianglai Dai, Xiaorui Shen, Yunchong Liu and Yuchen Cao
Computation 2025, 13(8), 186; https://doi.org/10.3390/computation13080186 - 4 Aug 2025
Abstract
Social media platforms have emerged as valuable sources for mental health research, enabling the detection of conditions such as depression through analyses of user-generated posts. This manuscript offers practical, step-by-step guidance for applying machine learning and deep learning methods to mental health detection [...] Read more.
Social media platforms have emerged as valuable sources for mental health research, enabling the detection of conditions such as depression through analyses of user-generated posts. This manuscript offers practical, step-by-step guidance for applying machine learning and deep learning methods to mental health detection on social media. Key topics include strategies for handling heterogeneous and imbalanced datasets, advanced text preprocessing, robust model evaluation, and the use of appropriate metrics beyond accuracy. Real-world examples illustrate each stage of the process, and an emphasis is placed on transparency, reproducibility, and ethical best practices. While the present work focuses on text-based analysis, we discuss the limitations of this approach—including label inconsistency and a lack of clinical validation—and highlight the need for future research to integrate multimodal signals and gold-standard psychometric assessments. By sharing these frameworks and lessons, this manuscript aims to support the development of more reliable, generalizable, and ethically responsible models for mental health detection and early intervention. Full article
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25 pages, 374 KiB  
Article
Goodness-of-Fit Tests for Combined Unilateral and Bilateral Data
by Jia Zhou and Chang-Xing Ma
Mathematics 2025, 13(15), 2501; https://doi.org/10.3390/math13152501 - 3 Aug 2025
Viewed by 52
Abstract
Clinical trials involving paired organs often yield a mixture of unilateral and bilateral data, where each subject may contribute either one or two responses. While unilateral responses from different individuals can be treated as independent, bilateral responses from the same individual are likely [...] Read more.
Clinical trials involving paired organs often yield a mixture of unilateral and bilateral data, where each subject may contribute either one or two responses. While unilateral responses from different individuals can be treated as independent, bilateral responses from the same individual are likely correlated. Various statistical methods have been developed to account for this intra-subject correlation in the bilateral data, and in practice, it is crucial to select a model that properly accounts for this correlation to ensure accurate inference. Previous research has investigated goodness-of-fit test statistics for correlated bilateral data under different group settings, assuming fully observed paired outcomes. In this work, we extend these methods to the more general and practically common setting where unilateral and bilateral data are combined. We examine the performance of various goodness-of-fit statistics under different statistical models, including the Clayton copula model. Simulation results indicate that the performance of the goodness-of-fit tests is model-dependent, especially when the sample size is small and/or the intra-subject correlation is high. However, the three bootstrap methods generally offer more robust performance. In real world applications from otolaryngologic and ophthalmologic studies, model choice significantly impacts conclusions, emphasizing the need for appropriate model assessment in practice. Full article
54 pages, 506 KiB  
Article
Enhancing Complex Decision-Making Under Uncertainty: Theory and Applications of q-Rung Neutrosophic Fuzzy Sets
by Omniyyah Saad Alqurashi and Kholood Mohammad Alsager
Symmetry 2025, 17(8), 1224; https://doi.org/10.3390/sym17081224 - 3 Aug 2025
Viewed by 116
Abstract
This thesis pioneers the development of q-Rung Neutrosophic Fuzzy Rough Sets (q-RNFRSs), establishing the first theoretical framework that integrates q-Rung Neutrosophic Sets with rough approximations to break through the conventional μq+ηq+νq1 constraint of existing [...] Read more.
This thesis pioneers the development of q-Rung Neutrosophic Fuzzy Rough Sets (q-RNFRSs), establishing the first theoretical framework that integrates q-Rung Neutrosophic Sets with rough approximations to break through the conventional μq+ηq+νq1 constraint of existing fuzzy–rough hybrids, achieving unprecedented capability in extreme uncertainty representation through our generalized model (Tq+Iq+Fq3). The work makes three fundamental contributions: (1) theoretical innovation through complete algebraic characterization of q-RNFRSs, including two distinct union/intersection operations and four novel classes of complement operators (with Theorem 1 verifying their involution properties via De Morgan’s Laws); (2) clinical breakthrough via a domain-independent medical decision algorithm featuring dynamic q-adaptation (q = 2–4) for criterion-specific uncertainty handling, demonstrating 90% diagnostic accuracy in validation trials—a 22% improvement over static models (p<0.001); and (3) practical impact through multi-dimensional uncertainty modeling (truth–indeterminacy–falsity), robust therapy prioritization under data incompleteness, and computationally efficient approximations for real-world clinical deployment. Full article
(This article belongs to the Special Issue The Fusion of Fuzzy Sets and Optimization Using Symmetry)
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45 pages, 5594 KiB  
Article
Integrated Medical and Digital Approaches to Enhance Post-Bariatric Surgery Care: A Prototype-Based Evaluation of the NutriMonitCare System in a Controlled Setting
by Ruxandra-Cristina Marin, Marilena Ianculescu, Mihnea Costescu, Veronica Mocanu, Alina-Georgiana Mihăescu, Ion Fulga and Oana-Andreia Coman
Nutrients 2025, 17(15), 2542; https://doi.org/10.3390/nu17152542 - 2 Aug 2025
Viewed by 243
Abstract
Introduction/Objective: Post-bariatric surgery patients require long-term, coordinated care to address complex nutritional, physiological, and behavioral challenges. Personalized smart nutrition, combining individualized dietary strategies with targeted monitoring, has emerged as a valuable direction for optimizing recovery and long-term outcomes. This article examines how traditional [...] Read more.
Introduction/Objective: Post-bariatric surgery patients require long-term, coordinated care to address complex nutritional, physiological, and behavioral challenges. Personalized smart nutrition, combining individualized dietary strategies with targeted monitoring, has emerged as a valuable direction for optimizing recovery and long-term outcomes. This article examines how traditional medical protocols can be enhanced by digital solutions in a multidisciplinary framework. Methods: The study analyzes current clinical practices, including personalized meal planning, physical rehabilitation, biochemical marker monitoring, and psychological counseling, as applied in post-bariatric care. These established approaches are then analyzed in relation to the NutriMonitCare system, a digital health system developed and tested in a laboratory environment. Used here as an illustrative example, the NutriMonitCare system demonstrates the potential of digital tools to support clinicians through real-time monitoring of dietary intake, activity levels, and physiological parameters. Results: Findings emphasize that medical protocols remain the cornerstone of post-surgical management, while digital tools may provide added value by enhancing data availability, supporting individualized decision making, and reinforcing patient adherence. Systems like the NutriMonitCare system could be integrated into interdisciplinary care models to refine nutrition-focused interventions and improve communication across care teams. However, their clinical utility remains theoretical at this stage and requires further validation. Conclusions: In conclusion, the integration of digital health tools with conventional post-operative care has the potential to advance personalized smart nutrition. Future research should focus on clinical evaluation, real-world testing, and ethical implementation of such technologies into established medical workflows to ensure both efficacy and patient safety. Full article
(This article belongs to the Section Nutrition and Public Health)
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62 pages, 4641 KiB  
Review
Pharmacist-Driven Chondroprotection in Osteoarthritis: A Multifaceted Approach Using Patient Education, Information Visualization, and Lifestyle Integration
by Eloy del Río
Pharmacy 2025, 13(4), 106; https://doi.org/10.3390/pharmacy13040106 - 1 Aug 2025
Viewed by 125
Abstract
Osteoarthritis (OA) remains a major contributor to pain and disability; however, the current management is largely reactive, focusing on symptoms rather than preventing irreversible cartilage loss. This review first examines the mechanistic foundations for pharmacological chondroprotection—illustrating how conventional agents, such as glucosamine sulfate [...] Read more.
Osteoarthritis (OA) remains a major contributor to pain and disability; however, the current management is largely reactive, focusing on symptoms rather than preventing irreversible cartilage loss. This review first examines the mechanistic foundations for pharmacological chondroprotection—illustrating how conventional agents, such as glucosamine sulfate and chondroitin sulfate, can potentially restore extracellular matrix (ECM) components, may attenuate catabolic enzyme activity, and might enhance joint lubrication—and explores the delivery challenges posed by avascular cartilage and synovial diffusion barriers. Subsequently, a practical “What–How–When” framework is introduced to guide community pharmacists in risk screening, DMOAD selection, chronotherapeutic dosing, safety monitoring, and lifestyle integration, as exemplified by the CHONDROMOVING infographic brochure designed for diverse health literacy levels. Building on these strategies, the P4–4P Chondroprotection Framework is proposed, integrating predictive risk profiling (physicians), preventive pharmacokinetic and chronotherapy optimization (pharmacists), personalized biomechanical interventions (physiotherapists), and participatory self-management (patients) into a unified, feedback-driven OA care model. To translate this framework into routine practice, I recommend the development of DMOAD-specific clinical guidelines, incorporation of chondroprotective chronotherapy and interprofessional collaboration into health-professional curricula, and establishment of multidisciplinary OA management pathways—supported by appropriate reimbursement structures, to support preventive, team-based management, and prioritization of large-scale randomized trials and real-world evidence studies to validate the long-term structural, functional, and quality of life benefits of synchronized DMOAD and exercise-timed interventions. This comprehensive, precision-driven paradigm aims to shift OA care from reactive palliation to true disease modification, preserving cartilage integrity and improving the quality of life for millions worldwide. Full article
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17 pages, 1304 KiB  
Review
Treatment Strategies for First-Line PD-L1-Unselected Advanced NSCLC: A Comparative Review of Immunotherapy-Based Regimens by PD-L1 Expression and Clinical Indication
by Blerina Resuli, Diego Kauffmann-Guerrero, Maria Nieves Arredondo Lasso, Jürgen Behr and Amanda Tufman
Diagnostics 2025, 15(15), 1937; https://doi.org/10.3390/diagnostics15151937 - 31 Jul 2025
Viewed by 358
Abstract
Background: Lung cancer remains the leading cause of cancer-related mortality worldwide. Advances in screening, diagnosis, and management have transformed clinical practice, particularly with the integration of immunotherapy and target therapies. Methods: A systematic literature search was carried out for the period between [...] Read more.
Background: Lung cancer remains the leading cause of cancer-related mortality worldwide. Advances in screening, diagnosis, and management have transformed clinical practice, particularly with the integration of immunotherapy and target therapies. Methods: A systematic literature search was carried out for the period between October 2016 to September 2024. Phase II and III randomized trials evaluating ICI monotherapy, ICI–chemotherapy combinations, and dual ICI regimens in patients with advanced NSCLC were included. Outcomes of interest included overall survival (OS), progression-free survival (PFS), and treatment-related adverse events (AEs). Results: PD-1-targeted therapies demonstrated superior OS compared to PD-L1-based regimens, with cemiplimab monotherapyranking highest for OS benefit (posterior probability: 90%), followed by sintilimab plus platinum-based chemotherapy (PBC) and pemetrexed—PBC. PFS atezolizumab plus bevacizumab and PBC, and camrelizumab plus PBC were the most effective regimens. ICI–chemotherapy combinations achieved higher ORRs but were associated with greater toxicity. The most favorable safety profiles were observed with cemiplimab, nivolumab, and avelumab monotherapy, while atezolizumab plus PBC and sugemalimab plus PBC carried the highest toxicity burdens. Conclusions: In PD-L1-unselected advanced NSCLC, PD-1 blockade—particularly cemiplimab monotherapy—and rationally designed ICI–chemotherapy combinations represent the most efficacious treatment strategies. Balancing efficacy with safety remains critical, especially in the absence of predictive biomarkers. These findings support a patient-tailored approach to immunotherapy and highlight the need for further biomarker-driven and real-world investigations to optimize treatment selection. Full article
(This article belongs to the Special Issue Lung Cancer: Screening, Diagnosis and Management: 2nd Edition)
20 pages, 586 KiB  
Article
Implementing High-Intensity Gait Training in Stroke Rehabilitation: A Real-World Pragmatic Approach
by Jennifer L. Moore, Pia Krøll, Håvard Hansen Berg, Merethe B. Sinnes, Roger Arntsen, Chris E. Henderson, T. George Hornby, Stein Arne Rimehaug, Ingvild Lilleheie and Anders Orpana
J. Clin. Med. 2025, 14(15), 5409; https://doi.org/10.3390/jcm14155409 - 31 Jul 2025
Viewed by 263
Abstract
Background: High-intensity gait training (HIT) is an evidence-based intervention recommended for stroke rehabilitation; however, its implementation in routine practice is inconsistent. This study examined the real-world implementation of HIT in an inpatient rehabilitation setting in Norway, focusing on fidelity, barriers, and knowledge [...] Read more.
Background: High-intensity gait training (HIT) is an evidence-based intervention recommended for stroke rehabilitation; however, its implementation in routine practice is inconsistent. This study examined the real-world implementation of HIT in an inpatient rehabilitation setting in Norway, focusing on fidelity, barriers, and knowledge translation (KT) strategies. Methods: Using the Knowledge-to-Action (KTA) framework, HIT was implemented in three phases: pre-implementation, implementation, and competency. Fidelity metrics and coverage were assessed in 99 participants post-stroke. Barriers and facilitators were documented and categorized using the Consolidated Framework for Implementation Research. Results: HIT was delivered with improved fidelity during the implementation and competency phases, reflected by increased stepping and heart rate metrics. A coverage rate of 52% was achieved. Barriers evolved over time, beginning with logistical and knowledge challenges and shifting toward decision-making complexity. The KT interventions, developed collaboratively by clinicians and external facilitators, supported implementation. Conclusions: Structured pre-implementation planning, clinician engagement, and external facilitation enabled high-fidelity HIT implementation in a real-world setting. Pragmatic, context-sensitive strategies were critical to overcoming evolving barriers. Future research should examine scalable, adaptive KT strategies that balance theoretical guidance with clinical feasibility to sustain evidence-based practice in rehabilitation. Full article
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14 pages, 1279 KiB  
Article
Real-World Toxicity and Effectiveness Study of Abemaciclib in Greek Patients with Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer: A Multi-Institutional Study
by Elena Fountzilas, Eleni Aravantinou-Fatorou, Katerina Dadouli, Panagiota Economopoulou, Dimitrios Tryfonopoulos, Anastasia Vernadou, Eleftherios Vorrias, Anastasios Vagionas, Adamantia Nikolaidi, Sofia Karageorgopoulou, Anna Koumarianou, Ioannis Boukovinas, Davide Mauri, Stefania Kokkali, Athina Christopoulou, Nikolaos Tsoukalas, Avraam Assi, Nikolaos Spathas, Paris Kosmidis, Angelos Koutras, George Fountzilas and Amanda Psyrriadd Show full author list remove Hide full author list
Cancers 2025, 17(15), 2543; https://doi.org/10.3390/cancers17152543 - 31 Jul 2025
Viewed by 129
Abstract
Background/Objectives: This study aimed to assess real-world toxicity and efficacy data of patients with early and advanced breast cancer (BC) who received treatment with abemaciclib. Methods: This was a prospective/retrospective multi-institutional collection of clinicopathological, toxicity, and outcome data from patients with early or [...] Read more.
Background/Objectives: This study aimed to assess real-world toxicity and efficacy data of patients with early and advanced breast cancer (BC) who received treatment with abemaciclib. Methods: This was a prospective/retrospective multi-institutional collection of clinicopathological, toxicity, and outcome data from patients with early or metastatic hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative BC who received treatment with abemaciclib in combination with endocrine therapy in departments of oncology in Greece. Treatment combinations of abemaciclib with any endocrine therapy were accepted. The primary end point was toxicity rate in all patients of the study. Results: From June/2021 to May/2024, 245 women received abemaciclib/endocrine combination therapy; the median age was 57 years. Of these, 169 (69%) received abemaciclib as adjuvant therapy for early-stage disease, while 76 (31%) were treated for advanced BC. At the time of the data cutoff, 133 (84.7%) patients remained in the 2-year treatment period. The most common adverse event (AE) was diarrhea (51%), primarily Grade ≤ 2. Dose modifications due to AEs were required in 19.2% of cases, while treatment discontinuation occurred in 5.1%. There was no difference in dose modification/discontinuation rates between older patients (>65 years) and the remaining patients. For early-stage BC patients, the 2-year DFS and OS rates were 90.8% and 100%, respectively. In patients with advanced cancer (70, 30.8%), 1-year PFS and OS rates were 78% and 96.3%, respectively. Conclusions: This study confirms the safety and effectiveness of abemaciclib in alignment with registrational trials offering valuable insights into toxicity management and clinical outcomes in routine practice without identifying new safety concerns. Clinical Trial Registration: ClinicalTrials.gov NCT04985058. Full article
(This article belongs to the Section Cancer Survivorship and Quality of Life)
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15 pages, 2428 KiB  
Article
Using Large Language Models to Simulate History Taking: Implications for Symptom-Based Medical Education
by Cheong Yoon Huh, Jongwon Lee, Gibaeg Kim, Yerin Jang, Hye-seung Ko, Min Jung Suh, Sumin Hwang, Ho Jin Son, Junha Song, Soo-Jeong Kim, Kwang Joon Kim, Sung Il Kim, Chang Oh Kim and Yeo Gyeong Ko
Information 2025, 16(8), 653; https://doi.org/10.3390/info16080653 - 31 Jul 2025
Viewed by 127
Abstract
Medical education often emphasizes theoretical knowledge, limiting students’ opportunities to practice history taking, a structured interview that elicits relevant patient information before clinical decision making. Large language models (LLMs) offer novel solutions by generating simulated patient interviews. This study evaluated the educational potential [...] Read more.
Medical education often emphasizes theoretical knowledge, limiting students’ opportunities to practice history taking, a structured interview that elicits relevant patient information before clinical decision making. Large language models (LLMs) offer novel solutions by generating simulated patient interviews. This study evaluated the educational potential of LLM-generated history-taking dialogues, focusing on clinical validity and diagnostic diversity. Chest pain was chosen as a representative case given its frequent presentation and importance for differential diagnosis. A fine-tuned Gemma-3-27B, specialized for medical interviews, was compared with GPT-4o-mini, a freely accessible LLM, in generating multi-branching history-taking dialogues, with Claude-3.5 Sonnet inferring diagnoses from these dialogues. The dialogues were assessed using a Chest Pain Checklist (CPC) and entropy-based metrics. Gemma-3-27B outperformed GPT-4o-mini, generating significantly more high-quality dialogues (90.7% vs. 76.5%). Gemma-3-27B produced diverse and focused diagnoses, whereas GPT-4o-mini generated broader but less specific patterns. For demographic information, such as age and sex, Gemma-3-27B showed significant shifts in dialogue patterns and diagnoses aligned with real-world epidemiological trends. These findings suggest that LLMs, particularly those fine-tuned for medical tasks, are promising educational tools for generating diverse, clinically valid interview scenarios that enhance clinical reasoning in history taking. Full article
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15 pages, 1152 KiB  
Article
Nurse-Led, Remote Optimisation of Guideline-Directed Medical Therapy in Patients with Heart Failure and Reduced Ejection Fraction Across Australia
by Gabrielle Freedman, Racheal Watt, Enayet Karim Chowdhury, Kate Quinlan, David Eccleston, Andrea Driscoll, James Theuerle and Leighton Kearney
J. Clin. Med. 2025, 14(15), 5371; https://doi.org/10.3390/jcm14155371 - 30 Jul 2025
Viewed by 544
Abstract
Background/Objectives: Guidelines recommend patients with heart failure with reduced ejection fraction (HFrEF) receive four-pillar heart failure (4P-HF) therapy, which significantly reduces cardiac morbidity and mortality. However, implementing these guidelines effectively into clinical practice remains challenging. Methods: Patients with HFrEF on submaximal [...] Read more.
Background/Objectives: Guidelines recommend patients with heart failure with reduced ejection fraction (HFrEF) receive four-pillar heart failure (4P-HF) therapy, which significantly reduces cardiac morbidity and mortality. However, implementing these guidelines effectively into clinical practice remains challenging. Methods: Patients with HFrEF on submaximal 4P-HF therapy were identified from a large, multicentre Cardiology network database using a natural language processing tool, supported by manual file review. A nurse-led, remotely delivered, medication uptitration program aimed to optimise therapy in this real-world cohort. Results: The final cohort included 2004 patients with a mean age of 72.7 ± 11.6 years. Utilisation of 4P-HF increased from 11.1% at baseline to 49.8% post intervention, and each individual medication class increased significantly post intervention (all p < 0.001). The largest increase was observed with the use of sodium–glucose cotransporter 2 inhibitors, which rose from 17.3% to 73.9%, followed by mineralocorticoid receptor antagonists (51.6% to 65.7%), beta-blockers (88.4% to 97.0%), and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor blocker–neprilysin inhibitors (89.8% to 96.4%). In patients on submaximal therapy, barriers were documented in all cases. Following medication optimisation, left ventricular ejection function (LVEF) improved significantly (38.5% ± 10.8% vs. 42.5% ± 11.7, p < 0.001). Conclusions: This nurse-led, remotely delivered, medication optimisation program significantly improved the adoption of 4P-HF therapy and LVEF in patients with HFrEF. The program demonstrates a practical, scalable solution for the optimisation of HFrEF therapy across a large healthcare network. Full article
(This article belongs to the Section Cardiology)
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22 pages, 1724 KiB  
Article
Development and Clinical Interpretation of an Explainable AI Model for Predicting Patient Pathways in the Emergency Department: A Retrospective Study
by Émilien Arnaud, Pedro Antonio Moreno-Sanchez, Mahmoud Elbattah, Christine Ammirati, Mark van Gils, Gilles Dequen and Daniel Aiham Ghazali
Appl. Sci. 2025, 15(15), 8449; https://doi.org/10.3390/app15158449 - 30 Jul 2025
Viewed by 324
Abstract
Background: Overcrowded emergency departments (EDs) create significant challenges for patient management and hospital efficiency. In response, Amiens Picardy University Hospital (APUH) developed the “Prediction of the Patient Pathway in the Emergency Department” (3P-U) model to enhance patient flow management. Objectives: To develop and [...] Read more.
Background: Overcrowded emergency departments (EDs) create significant challenges for patient management and hospital efficiency. In response, Amiens Picardy University Hospital (APUH) developed the “Prediction of the Patient Pathway in the Emergency Department” (3P-U) model to enhance patient flow management. Objectives: To develop and clinically validate an explainable artificial intelligence (XAI) model for hospital admission predictions, using structured triage data, and demonstrate its real-world applicability in the ED setting. Methods: Our retrospective, single-center study involved 351,019 patients consulting in APUH’s EDs between 2015 and 2018. Various models (including a cross-validation artificial neural network (ANN), a k-nearest neighbors (KNN) model, a logistic regression (LR) model, and a random forest (RF) model) were trained and assessed for performance with regard to the area under the receiver operating characteristic curve (AUROC). The best model was validated internally with a test set, and the F1 score was used to determine the best threshold for recall, precision, and accuracy. XAI techniques, such as Shapley additive explanations (SHAP) and partial dependence plots (PDP) were employed, and the clinical explanations were evaluated by emergency physicians. Results: The ANN gave the best performance during the training stage, with an AUROC of 83.1% (SD: 0.2%) for the test set; it surpassed the RF (AUROC: 71.6%, SD: 0.1%), KNN (AUROC: 67.2%, SD: 0.2%), and LR (AUROC: 71.5%, SD: 0.2%) models. In an internal validation, the ANN’s AUROC was 83.2%. The best F1 score (0.67) determined that 0.35 was the optimal threshold; the corresponding recall, precision, and accuracy were 75.7%, 59.7%, and 75.3%, respectively. The SHAP and PDP XAI techniques (as assessed by emergency physicians) highlighted patient age, heart rate, and presentation with multiple injuries as the features that most specifically influenced the admission from the ED to a hospital ward. These insights are being used in bed allocation and patient prioritization, directly improving ED operations. Conclusions: The 3P-U model demonstrates practical utility by reducing ED crowding and enhancing decision-making processes at APUH. Its transparency and physician validation foster trust, facilitating its adoption in clinical practice and offering a replicable framework for other hospitals to optimize patient flow. Full article
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