Search Results (17,523)

Background/Objectives: Nurses’ engagement in research is essential to strengthen evidence-based practice, knowledge translation, and quality of care. However, individual, organisational, and cultural barriers may limit nurses’ participation in research activities. This study aimed to cross-culturally adapt and psychometrically test the Italian version of the Barriers to Nurses’ Participation in Research Questionnaire within the Italian cultural and healthcare organisational context, and to explore perceived obstacles to research engagement among nurses in Italy. Methods: A cross-sectional methodological study was conducted. The instrument was translated, back-translated, reviewed by the original instrument developer and an expert panel, and evaluated for content validity by 12 clinical research professionals. Data were collected online between September and October 2024 from 196 nurses working across Italian healthcare settings, including hospitals, university hospitals, IRCCS, primary care, and private hospitals. Exploratory Structural Equation Modelling was used to examine the factor structure. Results: A total of 196 nurses were enrolled in the study. A two-factor structure was identified, comprising Research Resources and Personal Relevance of Research, which explained 35.37% and 25.14% of the variance, respectively. Both factors demonstrated good reliability. The most prominent barrier was the lack of incentive or reward for nurses to engage in research, whereas the least relevant barrier was the perception that research was not interesting or valuable. Greater barriers were reported by younger nurses, those with fewer years of experience, and those without specific research training. Lack of time to conduct research emerged as a pervasive obstacle across the sample. Conclusions: The Italian version of the Barriers to Nurses’ Participation in Research Questionnaire provides preliminary evidence of validity and reliability for assessing perceived barriers to research participation among Italian nurses. Owing to the structural modifications introduced during adaptation, the instrument should be interpreted as a culturally adapted and modified Italian version rather than as a direct replication of the original structure. Its use may support organisational diagnosis, research mentorship, training planning, and future research-capacity-building initiatives, although further validation in larger and more heterogeneous samples is warranted. Full article

Introduction: Compassion is defined as the emotional response that arises when an individual perceives another’s suffering and is motivated to alleviate it. Purpose: To explore levels of self-compassion among nurses working in pediatric hospitals and examine their associations with nurses’ characteristics. Materials and Methods: This cross-sectional study included a convenience sample of 208 nurses from a public pediatric hospital. Data were collected through interviews using the Neff Self-Compassion Scale (SCS) which includes the following subscales: Self-Kindness, Common Humanity, Mindfulness, Self-Judgment, Isolation, and Over-Identification. The Greek-validated version of the instrument was used with acceptable internal consistency in the present sample (Cronbach’s alpha = 0.849). Data analysis included descriptive statistics and inferential tests (non-parametric comparisons and multiple linear regression), with statistical significance defined as p < 0.05. Results: The mean total Self-Compassion score was 83.24 ± 12.6 (range: 26–130). Regarding family-related factors, total Self-Compassion (p = 0.029), Common Humanity (p = 0.033), and Over-Identification (p = 0.041) were associated with the number of children. In relation to age, Self-Kindness (p = 0.033), Isolation (p = 0.005), and Over-Identification (p = 0.005) showed significant associations. Professional factors were also relevant, as Isolation was associated with total years of nursing experience (p = 0.032) and choice of nursing as a profession (p = 0.004), while Over-Identification was associated with years of experience in pediatric settings (p = 0.004) and choice of nursing as a profession (p = 0.049). Additionally, marital status was associated with Over-Identification (p = 0.045). Conclusions: Demographic and professional characteristics appear to influence the expression of Self-compassion. Healthcare organizations should implement targeted training programs to individualize professional development. Future research should explore work-related and personal factors influencing self-compassion to improve care quality and outcomes. Full article

Background/Objectives: Vaccine attitudes are an individual’s beliefs, feelings, and evaluations regarding vaccines. Limited research has examined how students in faith-based university settings organize these attitudes. This study looked at vaccination attitudes among students at a religious university where faith, science, family, and politics often influence how students think and make decisions. Methods: This study used Q-methodology to examine shared viewpoints about vaccination. A concourse of 240 statements was developed from published literature, public discourse, and student interviews, then reduced to a 37-statement-Q-set. Undergraduate students enrolled in an introductory nonmajors biology course completed digital Q-sorts. We analyzed the data using by-person factor analysis, along with principal components analysis and Varimax rotation. Follow-up interviews helped us interpret the factors. Results: Three viewpoints explained 59% of the study variance. The first viewpoint, Faith-Integrated Institutional Trust, showed strong trust in science, public health agencies, and religious leaders. People in this group saw vaccination as both a moral duty and a way to protect others. The second viewpoint, Skeptical Autonomy and Institutional Distrust, emphasized personal choice, family influence, and distrust of government and official vaccine information. The third viewpoint, Pragmatic Autonomy and Science Confidence, endorsed vaccines and scientific evidence while also prioritizing individual decision-making over mandates. Conclusions: Science alone does not explain vaccination attitudes among college students. Trust, identity, and personal autonomy also play an important role. Vaccine communication should therefore connect scientific evidence with students’ moral commitments, trusted relationships, and concerns about freedom, especially in settings where faith influences health decision-making. Full article

The existing literature highlights that artificial intelligence (AI) creates both hope and threat perceptions among managers and workers, particularly due to concerns about potential job losses and the negative effect on continued professional development. Employee trust in AI-based systems varies depending on their features and performance. Furthermore, regardless of the performance of such systems, some individuals are inherently opposed to AI, a phenomenon known as AI aversion. In this study, an Integrative AI Adoption Framework is developed, drawing upon principles from established theories, including the technology acceptance model, behavioral decision theory, and emotion-based frameworks, to assess how perceived usefulness and perceived ease of use, along with perceived threat, trust, and AI aversion, influence human resources (HR) professionals’ attitudes and behavioral intentions to use AI-based recruitment systems. In doing so, the study conceptualizes AI-based recruitment as a socio-technical system in which a technical subsystem (the system’s instrumental and AI-specific properties) and a social subsystem (the affective and trust-related responses of HR professionals) must be jointly considered to explain adoption. The model was tested using the partial least squares structural equation modeling (PLS-SEM) approach through survey-based data collected from 242 HR professionals. The study’s findings indicate that attitude plays an important role in shaping behavioral intention, and perceived usefulness is a key driver of attitude. AI aversion negatively influences attitudes, while trust has a twofold effect of reducing AI aversion and positively influencing attitude. Additionally, perceived threat significantly increases AI aversion, which is driven by concerns over job replacement and personal development. Full article

Over the past decade, developments in artificial intelligence (AI) have sparked a new wave of debate and research across nearly all areas of life, including education. In English as a Foreign Language (EFL) education, AI-based technologies are also widely adopted to support learners and instructors. This trend has led to numerous studies focused on understanding AI’s role in identifying potential opportunities and challenges. This study offers a systematic review of relevant research, highlighting the benefits and obstacles of AI use in the Saudi EFL context. About 60 peer-reviewed articles were selected following PRISMA guidelines. The findings reveal multiple opportunities for AI integration in Saudi Arabia, such as improved language skills, personalized learning experiences, increased self-regulated learning, boosted motivation and confidence among learners, expanded learning opportunities, and support for pedagogy and institutional performance. Major challenges include biased and inaccurate data, students’ overdependence on technology, ethical concerns, and a lack of technological skills among users. The study also suggests future directions, including localizing AI tools, conducting long-term impact studies, providing faculty and student training, and establishing ethical guidelines within institutions. Full article

Background/Objectives: Merkel cell carcinoma (MCC) is a rare but aggressive skin cancer with a 40% recurrence rate. However, reliable biomarkers for early recurrence detection or treatment guidance are lacking, especially for virus-negative tumors. Circulating tumor DNA (ctDNA), a fragment of tumor-derived cell-free DNA in blood, has emerged across multiple cancers as a minimally invasive precision biomarker to detect minimal residual disease (MRD); predict recurrence; and monitor treatment response. This review’s objective was to summarize recent advances in ctDNA as a tool for therapeutic decision-making in MCC, contextualized by findings in other malignancies. Methods: A comprehensive literature review was performed, focusing on studies published between 2016 and 2026 that evaluate ctDNA in MCC and other cancers. Key prospective trials, observational studies, and case reports were identified through PubMed and relevant conference proceedings. Data on ctDNA assay methods (tumor-informed vs. tumor-agnostic), clinical sensitivity, lead time for recurrence detection, and predictive value for therapy response were extracted and synthesized. Results: Across cancers such as colorectal, lung, and melanoma, ctDNA positivity after curative treatment predicts relapse months in advance of imaging and can guide adjuvant therapy decisions. In MCC, recent studies demonstrate that ctDNA levels correlate with MCC tumor burden and exhibit high sensitivity and specificity for clinically evident disease. Stage I-III MCC patients who were ctDNA-positive within four months of treatment had a 7.4-fold higher recurrence risk within the subsequent 12–18 months of follow-up. Serial ctDNA monitoring may enable earlier intervention in otherwise asymptomatic ctDNA-positive MCC cases, helping distinguish responders from non-responders. Conclusions: ctDNA is an emerging precision biomarker that offers significant prognostic and surveillance utility in MCC. It enables earlier detection of recurrence, potentially allowing treatment to begin before clinical disease manifests. It also helps stratify patients by risk and treatment response, informing personalized surveillance intensity and therapeutic choices. Integrating ctDNA monitoring into MCC management could improve outcomes by guiding timely interventions, although prospective trials are needed to confirm that ctDNA-guided decisions translate to improved patient survival. Formal cost-effectiveness analyses have not yet been conducted and represent an important area for future investigation. Full article

Adverse imaging conditions such as fog, rain, and low light degrade the reliability of vision-based Personal Protective Equipment (PPE) detection systems on construction sites, yet most existing models are trained under clear-weather assumptions. This paper introduces a physics-based weather augmentation framework integrated with the YOLOv8n architecture to improve PPE detection robustness under adverse environmental conditions. The original Color Helmet and Vest (CHV) dataset was expanded from 1330 clear-weather images to 6650 images across five conditions using four physically grounded augmentation models: the Koschmieder atmospheric scattering model for fog, the Garg–Nayar streak model for rain, gamma-corrected attenuation with Poisson–Gaussian noise for low light, and a PSF-based glare model for bright sunlight. The weather-resistant model, a clear-weather baseline, and an augmented baseline were evaluated on the same 665-image weather-augmented test set. The weather-resistant model achieves 89.2% mAP50, a 5.7 percentage-point improvement over the clear-weather baseline (83.5%), with a nearly four-fold improvement in cross-condition stability (standard deviation 1.5% vs. 5.7%). Under matched training-data volume, the weather-resistant model still outperforms a conventionally augmented baseline across all five simulated conditions, indicating that these gains stem from physics-based modeling rather than larger training-data volume. The largest gain occurs under low light, where mAP50 improves from 73.4% to 87.9%. Gradient-weighted Class Activation Mapping (Grad-CAM) analysis confirms that the weather-resistant model directs more attention toward PPE regions across all conditions, with the largest improvement under low light (+10.0 percentage points). The lightweight design (3.0 M parameters) and quantitative and qualitative validation on 205 annotated real-world construction site images under normal and low-light conditions provide preliminary evidence of practical applicability. Full article

This paper examines the role of Multimodal Journey Planners (MJPs) as a link between user-oriented personalization and the broader societal goals of sustainable urban mobility. In smart cities, MJPs may serve as digital decision-support tools that connect individual mobility choices with broader sustainability objectives. Although contemporary journey planners increasingly display multiple criteria, such as travel time, cost, CO₂ emissions, and number of transfers, they still generally rely on predefined and non-personalized criterion weights and rarely infer travellers’ actual preferences from observed choices. The paper therefore proposes a transparent methodological proof-of-concept that combines multicriteria decision-making and inverse optimization to discover individual preference weights and enable personalized, preference-aware planning of multimodal routes. The Weighted Sum Method (WSM) is adopted as the basic ranking framework, and the proposed approach is evaluated within a controlled methodological testbed based on multimodal journey scenarios in Vienna. The results indicate that, within the available methodological testbed, the preference-discovery-based model achieved closer in-sample agreement with user-provided route evaluations than the model based on explicitly rated criteria. This was observed in the ranking-agreement analysis, where a more favourable penalty-point ratio was obtained in 19/21 cases (90.5%) and in the numerical error comparison, where lower in-sample reconstruction errors were obtained for 18/21 users (85.71%) across all scenarios. The paper further considers the tension between individual and system-level goals, as well as a conceptual extension toward system-aware re-ranking of alternatives. Within the broader framework of smart mobility, the importance of interoperability and open data is also recognized, with National Access Points (NAPs) for multimodal travel information potentially representing an important precondition for the development of advanced and transparent MJP solutions. Full article

Background/Objectives: The rising prevalence of multimorbid and complex chronically ill young and middle-aged adults necessitates the implementation of innovative care models and the creation of roles that can meet the complex healthcare needs of this patient group. Advanced Practice Nurses (APNs) can play a crucial role in the care of multimorbid and complex chronically ill young and middle-aged adults in APN-led clinics; however, in Switzerland, these roles are still evolving. The aim of this study was to explore APNs’ perspectives on the planned development of their roles in an APN-led clinic. Methods: To gain insights into the experiences of APNs in caring for this patient group, a qualitative study design was chosen. Data were collected through interviews with APNs from Switzerland, the USA, and Canada. In total, 19 APNs (12 from Switzerland and 7 from the United States and Canada) participated in the study. The data were collected through semi-structured online interviews. These data were analyzed using reflective thematic analysis in accordance with the approach presented by Braun and Clarke. Results: The analysis identified 10 themes that describe the competencies, components, and framework conditions required for the work of APNs in an APN-led clinic for multimorbid and complex chronically ill young and middle-aged adults within the Swiss clinical context. Required competencies include direct clinical practice, guidance and coaching, collaboration, and psychosocial support. Essential components include person-centered care, transitional care, and continuity of care. Key framework conditions include regulations of the legal and regulatory framework and eligibility for reimbursement of services, resources, and extended competencies and scope of practice. Conclusions: The perspectives of the APNs involved in this study show that multimorbid and complexly chronically ill young and middle-aged adults require complex and long-term care that extends beyond the hospital setting. The findings of this study show that Swiss APNs may be well positioned to contribute to this role. Full article

Background: Despite being considered a country with a larger health workforce in Africa, the South African health workforce continues to experience shortages and a maldistribution of health workers across regions and sectors. Current projections suggest that the workforce is expected to decline further, especially among doctors, nurses and midwives, in large part, due to attrition—which could compromise the delivery of primary health and maternity services. These health workforce shortages and uneven distribution threaten the sustainability and effectiveness of health services in South Africa and drives the need to investigate the factors that may be influencing career choice and change decisions among health professionals in South Africa. Methods: A qualitative exploratory study, making use of purposive sampling and semi-structured interviews, was conducted to investigate the factors influencing career choice and change decisions among health professionals in South Africa. The participants were qualified health professionals in the fields of medicine, nutrition, pharmacy, nursing, and psychology working in the private, public, and academic sectors. Data was collected until saturation was achieved and then thematically analyzed using MAXQDA 24. Results: A total of 10 participants made up of three males and seven females were interviewed. These participants worked in different employment sectors with some having dual roles in private practice, public sector, and academia. The analysis revealed three major themes that capture the nature of and factors influencing career choice and career changes occurring in South Africa. The first theme related to factors influencing career choice (including altruism, family influence, personal experiences, financial/job security, academic achievement, career guidance, and opportunity for change). The second theme focused on career change dynamics (nature of career changes and career transitions occurring in the form of specialization, switching health professions, exiting health professions, adding non-health interests, and shifting focus areas). The third theme revealed factors influencing career change. These were categorized into personal and individual factors, workplace or job-specific factors, and administrative factors. This study has contributed to understanding the career choices and career changes taking place within the health professions in South Africa. It has also revealed a need for reforms in policy and practice for the current health professionals who have no intention of changing their careers while highlighting implications for future training of health professionals. Also, addressing the challenges of poor working conditions, lack of support, unemployment and placement delays, and other administrative barriers will help mitigate some of the issues leading to health workforce shortages and inequities in the South African context. Conclusions: The strongest motivator for choosing a career in health professions is the desire to care for others, while retention of the health workforce is challenged by personal, workplace, and administrative factors. Enhancing workplace conditions and support systems, implementing policy reforms, and minimizing administrative barriers is essential for achieving universal health coverage and sustaining a resilient health workforce in South Africa. Full article

With the widespread adoption of intelligent terminals, edge devices, and distributed information systems in the financial domain, financial security sensing data exhibit multisource heterogeneity, dynamic temporal patterns, and high privacy sensitivity. Traditional centralized anomaly detection methods are no longer able to simultaneously satisfy the requirements of cross-institutional or cross-node collaborative modeling, client data privacy protection, and robust monitoring of transaction and system anomalies. To address this challenge, a data-local federated deep anomaly detection framework has been proposed for distributed financial security sensing systems. Initially, a local deep financial security sensing representation module is constructed to perform temporal encoding and attention-based modeling on multisource financial signals, including terminal operation status, network transaction communication, backend server operation, identity authentication, and anomaly alerts, thereby extracting representations relevant to anomalous behaviors. Subsequently, a data-local federated optimization and personalized aggregation mechanism is developed to enable cross-node knowledge sharing without transmitting raw transaction or client data, while local personalized detection heads are employed to adapt to non-independent and identically distributed (non-IID) financial institution data. Furthermore, an adversarially robust security detection and trust-aware aggregation strategy is introduced to enhance model stability under input noise, feature masking, anomaly camouflage, and potential malicious client updates. Experimental results demonstrate that the proposed method achieves an Accuracy of $92.37\%$, a Precision of $89.41\%$, a Recall of $88.26\%$, an F1-score of $88.83\%$, an AUC of $93.06\%$, and a PR-AUC of $89.15\%$ in the primary financial anomaly detection task, significantly outperforming baseline methods such as Isolation Forest, Autoencoder, LSTM, Transformer, FedAvg, FedProx, SCAFFOLD, and MOON. In robustness experiments, the method attains F1-scores of $87.95\%$, $86.42\%$, $86.88\%$, $84.57\%$, $86.73\%$, and $83.91\%$ under Gaussian noise, feature masking, temporal shift, adversarial perturbation, and $20\%$ and $30\%$ malicious client scenarios, respectively. Ablation studies further confirm the effectiveness of local representation learning, personalized federated optimization, adversarial training, and trust-aware aggregation mechanisms. Overall, the proposed approach provides an efficient intelligent anomaly detection solution for financial AI security monitoring scenarios characterized by data localization requirements, node heterogeneity, and attack perturbations. Full article

Background/Objectives: The trajectory of influenza hospitalization burden from pre-COVID-19 pandemic baseline through post-pandemic recovery remains poorly characterized at the national level. This study characterized phase-stratified burden and seasonal structure, quantified racial and ethnic disparities, and assessed whether post-pandemic seasons represent anomalous departures from pre-pandemic expectations. Methods: Sixteen complete seasons of FluSurv-NET surveillance data (2009–2010 through 2024–2025; 509 observation weeks) were analyzed across pre-pandemic, disruption, and recovery phases using OLS regression with effect-size estimation, bootstrapped age-adjusted rate ratios, seasonal-trend decomposition (STL), Prophet time-series forecasting, and Isolation Forest anomaly detection. Results: Mean peak weekly hospitalization rate nearly doubled from pre-pandemic to recovery (5.1 to 11.1 per 100,000), cumulative seasonal burden increased from 46.3 to 87.0 per 100,000, and median peak timing advanced from MMWR week 9 to week 50. STL decomposition revealed a marked shift from weak pre-pandemic seasonality (Fs = 0.14) to substantially stronger annual regularity (Fs = 0.98) across three recovery seasons, with threefold amplitude increase. Non-Hispanic Black persons had rate ratios of 1.72, 2.16, and 1.99 relative to White persons across phases; American Indian and Alaska Native persons showed the highest disruption-phase ratio (2.24, 95% CI 1.90–3.53), based on two contributing seasons. A flat-growth Prophet model detected first exceedance in February 2020, outperforming a linear-growth specification on held-out validation. Isolation Forest identified 2017–2018, 2023–2024, and 2024–2025 as robust anomalies across all contamination thresholds. Conclusions: Post-COVID-19 pandemic influenza recovery is characterized by intensified and restructured seasonality, persistent racial and ethnic disparities, and anomalous burden exceeding pre-pandemic projections, identified independently by time-series forecasting and unsupervised anomaly detection. Full article

Objective: Based on the Person-Centered Approach, this study targeted parents of very preterm (VPT) infants in Neonatal Intensive Care Units (NICUs) from Italy and Portugal. The primary aim was to classify parents by identifying latent classes of perceived risks (NICU stressors) and resources (sources of social support). Potential specificities in class membership according to Country of Belonging and sociodemographic factors were also investigated. Methods: Overall, 303 parents (92 Italian; 211 Portuguese) completed a survey including sociodemographic factors, Parental-Stressor-Scale-NICU, and Multidimensional-Scale-of-Perceived-Social-Support. Data were analyzed by multigroup latent class analysis and multinomial logistic regression. Results: Three statistically valid and cross-country classes were identified and labelled as Class 1, Adjusted/Beneficial-and-Supported-System, Class 2, Stressed-and-Supported-System, and Class 3, Parental-Role-Alteration-with-Family-Supported-System. Portuguese parents were mainly grouped in Classes 1 and 2, while Italian parents were in Class 3. Men were less likely to belong to Classes 2 and 3, while older parents having another child were more likely to belong to Class 3. Conclusions: The experience of parents of VPT infants in NICUs is inherently challenging, yet identifying specific risk profiles featured by the unique nuances of stressors and sources of support while accounting for further factors (Country of Belonging, Gender, Age, Having another child) can foster the customization of interventions aimed at providing parents with the necessary resources for adjusting to this extremely demanding experience. Full article

Background: The growing complexity of liquid biopsy in precision oncology demands a structured classification framework that can accommodate its expanding multi-omic scope. As the field has matured from early Tumor Microemboli research—focused on multicellular clusters of circulating tumor cells (CTCs) that drive high-efficiency metastasis—to the broader systemic analysis of the “Tumor Microenvironment” (TME) encompassing malignant and non-malignant components, the need for a hierarchical taxonomy has become evident. Objective: To integrate these diverse data streams into a coherent clinical framework, a multi-tiered classification system is needed. This review proposes a foundational roadmap that formally distinguishes the systemic ecosystem from its physical and functional subsets and highlights their clinical utility in therapeutic decision-making. Proposed Taxonomy: We advocate for the adoption of Circulating Tumor Microenvironment (cTME) as the inclusive term for the systemic environment, encompassing non-cellular factors such as ctDNA, extracellular vesicles, and biophysical attributes. Conversely, physical cellular clusters should be strictly classified as Circulating Tumor Emboli (CTE). Crucially, we define Circulating Tumor-Associated Cells (C-TACs) as the functional cellular subset within the cTME, encompassing single CTCs, CTE, and supporting non-malignant cells like CTECs and CAFs. Clinical Applications: Establishing this distinction allows for the seamless integration of molecular profiling (NGS) and functional assays. We highlight emerging evidence that C-TACs may serve as the primary substrate for Chemo-Response Profiling (CRP), with early proof-of-concept studies reporting high concordance with clinical outcomes that still await independent prospective confirmation. Furthermore, preliminary evidence suggests that identifying these functional units, particularly perioperative CTE, may help predict the efficacy of adjuvant chemotherapy in early-stage malignancies, although this remains to be confirmed in prospective studies. Conclusions: Adopting this unified taxonomy may help advance precision oncology. By recognizing the cTME as the superordinate ecosystem and C-TACs as its functional executors, clinicians may be better positioned to interpret multi-modal liquid biopsy data, providing a conceptual roadmap for integrating these technologies into platforms for personalized cancer management. We emphasize that this framework is intended to be hypothesis-generating and that its clinical applications require prospective validation before routine adoption. Full article

Immune thrombocytopenia (ITP) is a heterogeneous autoimmune disorder characterized by immune-mediated platelet destruction and impaired platelet production. Increasing evidence suggests that systemic inflammation plays a significant role in disease pathogenesis and clinical outcomes. This study aimed to evaluate the prognostic significance of inflammatory indices and their association with complications, mortality, treatment response, and relapse in patients with ITP. In this single-center retrospective study, 166 adult patients diagnosed with primary ITP between January 2015 and December 2024 were analyzed. Demographic, clinical, and laboratory data at diagnosis were collected. Inflammatory indices derived from complete blood count parameters, including neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), were evaluated. Their associations with clinical outcomes were assessed using appropriate statistical methods. During the observation period based on retrospective medical records, complications occurred in 12% of patients, and mortality was observed in 6.6%. Patients with complications had significantly higher D-dimer levels and reduced bone marrow megakaryocyte production. In group comparisons, mortality was significantly associated with advanced age, male sex, and comorbidities. Laboratory findings revealed that lower hemoglobin, lymphocyte count, mean platelet volume, and albumin levels, along with higher PLR, erythrocyte sedimentation rate, bilirubin, and D-dimer levels, were significantly associated with mortality. Inflammatory indices such as NLR and PLR were not associated with complication development, but PLR was significantly associated with mortality. Response to intravenous immunoglobulin (IVIG) therapy was significantly associated with higher total protein, albumin, and fibrinogen levels, and lower erythrocyte sedimentation rate. Relapse was significantly associated in group comparisons with increased inflammatory activity, higher reticulocyte count, and positivity for antinuclear antibodies and Helicobacter pylori antigen. Systemic inflammation and impaired megakaryopoiesis play critical roles in the prognosis of ITP. While conventional inflammatory indices showed limited predictive value for complications, markers such as PLR, D-dimer, and albumin were associated with mortality and clinical outcomes. These findings suggest that readily available laboratory parameters may provide valuable insights for risk stratification and personalized management in patients with ITP. Full article