Search Results (4,264)

Background/Objectives: The aim of this investigation was to determine postgraduate orthodontic students’ moral reasoning ability and evaluate their opinions on professionalism and the Dental Board of Australia’s (DBA) tribunal outcomes. Methods: Students undergoing postgraduate orthodontic specialist training in five Australian universities were invited to participate in a three-part electronic questionnaire survey. Part 1 related to participant demographic details. Part 2 invited responses to a series of statements related to professionalism and 10 DBA tribunal outcomes, and Part 3 was a validated moral reasoning assessment instrument [Defining Issues Test (DIT-2)]. Results: A response rate of 80.4% (n = 37) was recorded. The mean DIT-2 Post-conventional Schema (P) score was 31.14 (SD = 13.25). All respondents (n = 37; 100%) were aware of the DBA’s Code of Conduct (Code). Respondents were broadly supportive of the statements related to professionalism although 15.4% reported that being bound by the Code meant they could not fully value the student experience whilst at university. Most respondents considered that most DBA tribunal outcomes were fair or tended towards being lenient. However, one outcome regarding dishonest evidence by a dentist in court related to an employment dispute was considered harsh or very harsh by 53% (n = 20). There was little correlation between P scores and responses to most professionalism statements/DBA outcomes. Conclusions: The moral reasoning scores were lower than what might be expected from individuals in the provision of healthcare. The introduction of formal training in moral reasoning may develop postgraduate moral reasoning skills and DIT-2 scores. Full article

Background and Objectives: Perioperative fluorouracil, leucovorin, oxaliplatin, and docetaxel (FLOT) is the standard of care for resectable gastric and gastroesophageal junction adenocarcinoma; however, up to 50% of patients develop metastatic recurrence. These patients have prior exposure to platinum and taxane agents, and optimal first-line treatment in the metastatic setting remains undefined. This study aimed to characterize real-world treatment patterns and outcomes in patients progressing after perioperative FLOT, focusing on relapse timing and HER2 status. Materials and Methods: This retrospective, multicenter cohort study included 296 patients from 31 centers across Türkiye, stratified into early relapse (≤6 months, n = 114) and late relapse (>6 months, n = 182) groups. Survival analyses were performed using the Kaplan-Meier method and Cox proportional hazards regression. Primary endpoints were progression-free survival (PFS) and overall survival (OS). Results: Median PFS and OS for the entire cohort were 6 and 9 months, respectively. Early relapsers had significantly shorter median PFS (4 vs. 6 months, p = 0.029) and OS (8 vs. 12 months, p = 0.047); however, early relapse timing did not retain independent prognostic significance on multivariable analysis. No significant difference in PFS or OS was observed between cytotoxic chemotherapy regimens in either relapse group. HER2 positivity was the only independent predictor of improved PFS on multivariable Cox analysis (HR 0.48, 95% CI 0.29–0.81; p = 0.006). In the late relapse group, trastuzumab-based chemotherapy achieved a median PFS of 14 months and OS of 18 months, significantly superior to all cytotoxic regimens (PFS p = 0.007; OS p = 0.029). Conclusions: In patients progressing after perioperative FLOT, cytotoxic chemotherapy regimen selection did not demonstrate a statistically significant survival difference in this retrospective cohort, regardless of relapse timing. HER2 positivity is the dominant predictive biomarker, and trastuzumab-based therapy suggests a potential survival benefit that warrants prospective validation. Comprehensive biomarker profiling at metastatic diagnosis and prospective trials designed for this post-FLOT population are needed to establish evidence-based treatment standards. Full article

Depression is a critical global public health challenge, and the demand for accurate automated detection methods has generated considerable research interest in Transformer-based models. Despite their substantial promise, a comprehensive investigation into their architectural efficacy, intrinsic mechanisms, and barriers to practical implementation remains lacking. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, this systematic review was conducted across six databases (IEEE Xplore, Elsevier, Springer, MDPI, PubMed, and arXiv). The final search was performed in October 2025, covering English-language empirical studies published between 2020 and 2025 that employed Transformer-based architectures for depression detection. Risk of bias and methodological quality were independently appraised by two authors using a six-dimension structured rubric, with disagreements resolved by a third author. Findings were narratively synthesized given substantial cross-study heterogeneity. This systematic review analyzed 46 studies and provided the first comprehensive, mechanism-level, architecturally stratified comparison of encoder-only, decoder-only, hybrid, and multimodal fusion paradigms, examining self-attention dynamics and transfer learning strategies. Since 2019, these frameworks have evolved from text-centric approaches to advanced multimodal systems. Encoder-only models show consistently strong results in high-throughput text-based screening, decoder-only models demonstrate stronger few-shot learning capabilities, hybrid architectures show the highest observed median performance in clinical interview settings across the reviewed studies, and multimodal fusion systems offer complementary advantages when heterogeneous signal integration is critical. These trends are task-contextualized and should not be interpreted as unconditional rankings, given heterogeneity in evaluation metrics and tasks across studies. Nonetheless, four principal challenges hinder clinical translation: overreliance on self-reported data, cross-linguistic bias, absence of uncertainty quantification, and substantial computational overhead. Future efforts should shift from incremental benchmark improvements toward clinical utility through standardized psychiatric validation, uncertainty-aware architectures, fairness-enforced training across diverse populations, and the integration of Transformer-based models with wearable and mobile health data to improve detection stability and reduce translational risk. This systematic review was registered on the Open Science Framework (OSF; DOI: 10.17605/OSF.IO/SYF9N). This research was funded by the Faculty of Information Science and Technology and by Universiti Kebangsaan Malaysia under Grant TAP-K014364. Full article

Introduction: Humanized care is a core indicator of nursing quality, yet its prevalence and determinants among Spanish undergraduates remain unclear. Methods: A cross-sectional survey was administered to fourth-year nursing students from public and private universities. Instruments included the Health Professional’s Humanization Scale (HUMAS), the Communication Styles Inventory-Revised (CSI-R) and a sociodemographic questionnaire that captured prior training: completion of ≥6 h role-playing seminars in patient–family communication. Results: Mean scores were 3.62 ± 0.48 for HUMAS and 2.50 ± 0.52 for CSI-R. Women exceeded men on HUMAS total (p = 0.025) and on Sociability, Emotional Understanding, Dispositional Optimism and Self-Efficacy (all p ≤ 0.013), but not on Affect-Regulation or CSI-R. Age correlated weakly with Optimism (r = 0.24) and Self-Efficacy (r = 0.21). Students who had completed the role-playing seminars recorded higher HUMAS totals (d = 0.50; p = 0.001) and sub-scores, with only a modest gain in Affect-Regulation, and showed a trend towards better CSI-R performance (p = 0.06). No differences emerged by university type. HUMAS and CSI-R correlated moderately (r = 0.32; p = 0.001). In multivariate analysis, training (β = 0.36; p = 0.001) and CSI-R (β = 0.26; p = 0.001) jointly explained 27.9% of humanization variance; male sex exerted a small negative effect (β = −0.19; p = 0.001), whereas age was nonsignificant. Conclusions: Structured communication seminars are a key factor associated with higher levels of humanization in senior nursing students, whereas sociodemographic influences are modest. Embedding longitudinal, simulation-rich modules in communication and emotional intelligence is therefore recommended to cultivate truly person-centered nurses and to narrow observed sex disparities. Full article

Background/Objectives: In South Korea, physician assistant (PA) nurses—registered nurses performing physician-delegated advanced clinical tasks without a nationally standardized licensure system—are increasingly relied upon to address healthcare delivery gaps. This study examined the associations of organizational climate and role conflict with job esteem among PA nurses. Methods: A cross-sectional descriptive design was used. Data were collected from 145 PA nurses at four university hospitals (each ≥ 500 beds) in Daegu, South Korea, between March and April 2025, using validated instruments for organizational climate, role conflict, and job esteem. Multiple linear regression analysis was performed to identify factors associated with job esteem. Results: Organizational climate was positively correlated with job esteem, while role conflict showed a negative correlation. In the multiple linear regression model, organizational climate and pre-role training experience emerged as significant factors associated with job esteem, jointly explaining 23% of the variance, whereas role conflict did not show an independent association when organizational climate was included in the model. Conclusions: These findings suggest that supportive organizational climates and structured preparation may be important for sustaining job esteem among nurses working in expanded physician-delegated roles. In the broader context of physician shortages, which can compromise care quality and intensify nurses’ workload, strengthening organizational supports for PA nurses is also relevant to maintaining the quality and continuity of healthcare services. These findings may also be informative for other healthcare systems in which nursing role expansion is occurring faster than the development of supporting institutional structures. Full article

Purpose: Access to assistive technology (AT) is a fundamental human right and a critical component of Universal Health Coverage (UHC). In the Middle East and North Africa (MENA) region, the scarcity of trained professionals remains a significant barrier to AT service provision. This study evaluates the effectiveness and perceived usefulness of the Assistive Technology Training Program (ATTP), a specialized continuing education initiative designed to build workforce capacity among rehabilitation and healthcare professionals. Methods: A convergent mixed methods design was used to analyze quantitative pre/post-test scores and qualitative focus group open-ended responses. Quantitative data were gathered from 386 participants across 11 MENA countries using a pre- and post-test assessment of AT knowledge. Qualitative utility and participant satisfaction were assessed through a 5-point Likert scale survey evaluating content relevance, trainer expertise, and facilities. Association tests (ANOVA and t-tests) were conducted to identify factors influencing knowledge gain. Results: Participants demonstrated a statistically significant improvement in AT knowledge, with the overall mean score increasing from 3.67 ± 1.13 to 7.50 ± 1.25 (p < 0.001). High levels of satisfaction were reported, with 92% of participants rating the training as “Very Good” or “Excellent” regarding its relevance to clinical needs. Association tests revealed that professional background (p < 0.001), employment status (p = 0.0017), level of education (p = 0.011), and prior training experience (p = 0.026) were significant factors in the magnitude of improvement, although all subgroups achieved significant learning gains. Qualitative thematic analysis per the focus group discussions using the WHO-GATE 5 P framework identified three major themes: (1) Structural Challenges: Issues with Products and Provision point toward a need for better infrastructure and localized supply chains. (2) Human Capital: Personnel barriers emphasize that training shouldn’t just be for professionals, but should extend to caregivers as well. (3) Systemic and Social Change: Policy and People focus on the “soft” side of AT moving toward user-involved guidelines and fighting social stigma to ensure rights are upheld. Conclusions: The ATTP is an impactful educational intervention that significantly enhances the foundational competencies of healthcare professionals in the MENA region. By addressing knowledge gaps and fostering practical skills, the program serves as a preliminary model that demonstrates potential for building regional capacity and supporting the United Nations’ Sustainable Development Goal (SDG) #3 related to health and wellbeing and SDG #4 related to quality education and lifelong learning opportunities for all. Further research is required to evaluate its long-term scalability and clinical impact. Full article

Background/Objectives: Esophageal cancer is one of the major global causes of cancer mortality, and the 5-year survival rate remains below 20% because many cases are detected late. In this study, a Spectral-Aided Vision Enhancer (SAVE) algorithm was utilized to convert conventional white-light endoscopic images (WLI) into hyperspectral-like narrow-band imaging (NBI) images for machine-learning classification of Dysplasia, Normal, and Squamous Cell Carcinoma (SCC). Methods: A total of 762 WLI images obtained from Kaohsiung Medical University were augmented to 1074 using the Al bumentations library, employing vertical flipping, horizontal flipping, and rotations. The SAVE conversion pipeline employs a 24-patch Macbeth color checker for calibration, γ-correction, CIE XYZ transformation, and multivariate regression to interpolate spectral bands, yielding an average color difference of 2.79 (CIEDE2000) from true NBI. The training outcomes and performance metrics illustrate the versatility of the machine learning/deep learning models—Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)—which were trained and evaluated on both the original WLI and SAVE datasets. Performance metrics were analyzed based on precision, recall, accuracy, and F1-score. Results: The CNN sample achieved an accuracy of 100 percent on SAVE data, compared to 93 percent for WLI. The accuracy of RF improved, with WLI at 91% and SAVE at 96%, while SVM increased from 79% to 84%. These improvements indicate the diagnostically valuable spectral variations that can be amplified with SAVE, resulting in significant enhancements in pre-cancer/SCC sensitivity. Conclusions: The proposed SAVE method demonstrates significant potential for enhancing endoscopic imaging and advancing computer-aided diagnosis in esophageal cancer screening, with applicability in other gastrointestinal imaging scenarios as well. Full article

Time of day is often treated as a scheduling constraint rather than a true programming variable, yet training time is not neutral and can modulate both performance expression and the biological cost of exercise. This structured narrative review synthesizes evidence on morning versus afternoon/evening training in athletes and physically active adults, integrating performance outcomes with internal load, autonomic recovery, sleep interaction, and downstream physiological stress. Acute evidence generally supports superior late-afternoon or early-evening performance, particularly for neuromuscular tasks, whereas chronic training studies do not indicate a universal optimal training time. Instead, adaptation appears to depend on chronotype, habitual training time, the wake-to-training interval, light exposure, nutritional timing, training–testing congruency, and competition-specific demands. Mechanistically, exercise timing likely interacts with BMAL1/CRY loops, AMPK-SIRT1-PGC-1alpha signaling, PGC-1alpha-driven mitochondrial remodeling, and redox-inflammatory pathways, although much of this evidence remains preclinical. Accordingly, this review advances a Performance–Biological Cost (PBC) framework in which training time is judged by the balance between output, recovery opportunity, and residual molecular cost rather than by acute performance alone. By integrating athlete-relevant human evidence with mechanistic insights and translating them into explicit decision tools and testable predictions, this review positions exercise timing as a context-dependent lever within periodized training design. Full article

Background: The past decade has seen a reversal in the U.S long-term decline in age-adjusted mortality rate from stroke. Timely stroke detection can boost the patient’s chances for recovery by enabling life-saving treatment and informing the patient of their increased risk of successive infarcts. Since no single imaging modality can currently provide accurate and safe stroke detection at both acute and chronic stages, there is a need to develop novel imaging biomarkers with both diagnostic and prognostic value. Methods: We trained a U-shaped, nested hierarchical transformer model (UNesT) for T1-w white matter infarct segmentation using the ATLAS R2 dataset. Model reproducibility was independently evaluated on the Washington University (WU) stroke dataset. To boost T1-w UNesT stroke detection performance, automated machine learning techniques were used to extract 77 novel resting state fMRI (rs-fMRI) stroke biomarkers. Results: Stroke detection performance of the T1-w UNesT model degraded from Dice indices of 0.611 to 0.24 and 0.41 for the subacute and chronic timepoints respectively in the WU dataset. After UNesT re-optimization with the training portion of the WU dataset, the test set Dice index improved to 0.41–0.50. The spectral peak amplitude at the subacute timepoint increased the T1-w UNesT Dice index from 0.41 to 0.50 (p < 0.01) and correlated with language recovery. Conclusions: By training a UNesT model on the T1-w stroke data from one dataset and evaluating it on an independent dataset, we highlight the dataset drift concerns. Spectral peak amplitude is proposed as a novel rs-fMRI biomarker for improving stroke detection and predicting stroke recovery trajectory. Full article

Internet of Medical Things (IoMT) devices are inherently vulnerable to cyberattacks, typically due to their limited processing power and memory capacity. Their widespread use in healthcare poses a significant security risk, threatening patient data privacy and the continuity of services. This study examines the effects of data imbalance correction and balancing strategies on the performance of machine and deep learning models using openly available IoMT datasets. In this context, four different balancing methods—RandomUnderSampler, SMOTE, Borderline-SMOTE, and ADASYN—were applied to three open-access IoMT datasets: ECU-IoHT, WUSTL, and CICIoMT2024. Performance analyses were conducted using five machine learning algorithms (AdaBoost, Logistic Regression, Random Forest, XGBoost, and K-Nearest Neighbor (KNN)) and two deep learning algorithms (Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN)). In the highly imbalanced binary setting of the CICIoMT2024 dataset, the combination of RandomUnderSampler and SMOTE under the balanced-training/original-testing scenario produced the strongest improvement in the binary CICIoMT2024 setting, increasing the F1-Score from the unbalanced baseline to 99.87% for Random Forest and 99.86% for XGBoost across repeated runs. However, the benefit of balancing was not universal. In datasets with stronger class separability, such as ECU-IoHT, and in several multi-class settings, the effect of balancing was limited or, in some cases, inferior to the unbalanced baseline. These findings indicate that balancing is most effective under specific conditions, particularly in highly imbalanced binary tasks, and should be validated using class-sensitive metrics rather than overall performance alone. Full article

Accurate in-season rice (Oryza sativa L.) yield prediction is crucial for improved nitrogen management and climate-smart decision making, yet rigorous comparative benchmarking of machine learning (ML) models using multi-temporal UAV spectral data with independent temporal validation remains limited. This study systematically evaluated four ML algorithms (Random Forest, XGBoost, Neural Network, and Linear Regression) and two Bayesian model averaging ensembles for rice yield prediction using UAV multispectral imagery. Field experiments spanning three growing seasons (2023–2025) at Louisiana State University comprised 9–10 varieties and six nitrogen rates (0–235 kg N ha⁻¹; 576 plots). Vegetation indices and spectral bands from three growth stages were extracted as predictors. Models were compared using 300 random train–test iterations with systematic hyperparameter optimization, followed by independent validation on 2025 data. Among the individual models, XGBoost achieved the highest internal accuracy (R² = 0.87, RMSE = 0.85 t ha⁻¹), substantially outperforming Linear Regression (R² = 0.66, RMSE = 1.32 t ha⁻¹), while reduced BMA reached R² = 0.89. XGBoost demonstrated robust temporal generalization (R² = 0.62, NRMSE = 8.47%) despite environmental variation. The Enhanced Vegetation Index and Normalized Difference Red Edge at 90 days after planting (reproductive stage) were the most stable predictors across 300 iterations. Tree-based ML models substantially outperform traditional linear approaches, providing reliable pre-harvest yield forecasting for operational precision rice production. Full article

Accurate, rapid, and consistent evaluation of pavement condition across large-scale road networks is critical for sustainable maintenance and rehabilitation planning. However, conventional approaches largely rely on manual visual inspections, which are time-consuming, subjective, and difficult to implement at the network level. In this study, a semi-automated pavement distress evaluation framework that integrates field-based assessment with computer vision techniques is proposed. The study was conducted on a 3 km roadway network located within the Yıldız Technical University Davutpaşa Campus. Field-based distress observations were used as reference data, while street-level images obtained from the Mapillary platform were analyzed using a deep learning-based YOLOv8 model trained on the RDD2022 dataset, which was specifically developed for road distress detection. The analysis focuses on crack and pothole distress, which have a dominant influence on PCR and are highly distinguishable in image-based approaches. Correlation analyses between automated detection results and field-based data demonstrate a strong agreement, reaching values of approximately $\rho \approx 0.90$ in some routes. These findings indicate that these distress types are effective in representing variations in pavement condition. The results demonstrate that multi-source image data and deep learning-based detection methods can be reliably used for section-level pavement condition assessment. The proposed approach addresses a key gap in the literature by transforming image-level detections into engineering-based decision-support information. Furthermore, by leveraging publicly available data sources, the framework offers a low-cost and scalable solution that enables rapid preliminary assessment over large road networks, thereby providing significant potential for sustainable infrastructure management and the development of data-driven maintenance strategies. Several practical challenges encountered during the detection process—including sensitivity to contrast enhancement parameters, false positives from shadows and surface reflections, heterogeneous image resolution across crowdsourced imagery, and training distribution gaps for locally prevalent infrastructure features—are discussed, and directions for reducing human intervention through adaptive preprocessing and targeted model refinement are identified. Full article

Background: Nursing students are at high risk of exposure to blood and body fluids due to limited clinical experience. Ensuring adequate knowledge and proper post-exposure protocols is vital for improving safety and post-exposure management. Aim: This study aimed to evaluate the level of knowledge, previous exposure experience, and reporting practices regarding occupational exposure incidents among nursing students at the Faculty of Health Sciences, University of Split, Croatia. Methods: A cross-sectional study was conducted among 274 nursing students using a structured self-administered questionnaire. Descriptive statistical methods were applied, along with univariate and multivariate linear regression analyses. Results: Exposure incidents were experienced by 36.3% of students, with needlestick injuries being the most common (80.1%). In terms of reporting practices, fewer than half (40.8%) of those affected officially reported the incident. While students demonstrated adequate overall performance on the knowledge assessment (median score 12, IQR: 11–14), significant gaps were identified in hepatitis B and C protocols and immediate wound care. Multivariate analysis identified full-time student status (β = 1.24; p = 0.010) and first-year students (β = 0.82; p = 0.036) as factors significantly associated with higher knowledge scores. Conclusions: Although nursing students possess solid fundamental knowledge of exposure-related risks, a significant gap remains in their practical application and incident reporting. The high incidence of needlestick injuries (80.1%) underscores the importance of moving beyond theory toward enhanced clinical supervision. To address these gaps, nursing education should prioritize targeted practical training and cultivate a robust safety culture that encourages incident reporting. Full article

Background/Objectives: Postoperative nausea and vomiting (PONV) is a common complication that may negatively affect patient comfort and recovery following procedures performed under sedation. Although gastrointestinal endoscopic procedures are widely performed, data on the incidence and risk factors of PONV after esophagogastroduodenoscopy (EGD) remain limited. This study aimed to determine the incidence of PONV following EGD under sedation and to identify factors associated with its development. Methods: This single-center retrospective study included adult patients who underwent elective EGD under sedation between June and November 2023. Demographic and clinical data, Apfel risk scores, sedative agents, procedure duration, and macroscopic endoscopic findings were obtained from electronic medical records. PONV was assessed based on documentation during the post-anesthesia care unit stay. Patients were categorized into PONV-positive and PONV-negative groups and compared using appropriate statistical tests. Results: A total of 152 patients were included, and PONV occurred in 13 patients (8.6%). Female sex (p = 0.020), higher body mass index (BMI) (p = 0.009), preoperative nausea or vomiting (p = 0.002), thyroid disease (p = 0.004), oral antidiabetic drug use (p = 0.003), and higher Apfel risk scores (p = 0.008) were significantly associated with PONV. Age, American Society of Anesthesiologists (ASA) score, procedure duration, sedative agents, and macroscopic endoscopic findings showed no significant association. Conclusions: PONV following EGD under sedation was relatively uncommon. Patient-related factors, particularly female sex, higher BMI, preoperative nausea, thyroid disease, oral antidiabetic drug use, and higher Apfel scores, were associated with increased risk. Full article

Background: The transition from dental education to professional practice represents a critical stage in career development, influenced by individual expectations, labor market conditions, and institutional support mechanisms. This study aimed to explore final-year dental students’ perceptions of professional transition and the role of public authorities in facilitating early-career integration. Methods: A cross-sectional study was conducted among 216 final-year dental students from a single Romanian university using a structured, self-administered questionnaire. Descriptive and inferential statistical analyses were performed using Jamovi software, with significance set at p < 0.05. Results: Most students reported feeling insufficiently prepared for professional practice and identified lack of clinical experience as the main barrier to employment. A strong preference for private sector employment was observed, while interest in the public sector was limited. Students expressed a clear need for structured support, including mentorship, practical training, and career guidance. A significant association was identified between intention to work abroad and the types of support expected from authorities (χ²(2) = 14.7, p < 0.001, moderate effect size). Conclusions: The findings highlight important challenges in the transition to professional practice and emphasize the need for coordinated interventions involving educational institutions and public authorities. Strengthening structured support mechanisms may facilitate professional integration and contribute to improved workforce retention. Full article