Background/Objectives: Greek cancer patients deal with high out-of-pocket medical expenses in comparison with the European Union average. All these high costs affect the quality of life of cancer patients, leading to financial toxicity. The purpose of this study is to investigate the association between financial toxicity and quality of life in patients undergoing cancer treatment. Methods: A cross-sectional study was conducted in four hospitals in Greece. The Comprehensive Score for Financial Toxicity (COST) Scale was used for the evaluation of financial toxicity, and the EORTC Core Quality of Life questionnaire (EORTC QLQ-C30) was used for the assessment of quality of life. Quantitative variables were first tested for normality using the Kolmogorov–Smirnov criterion. Spearman correlation coefficients (rho) were used to explore the association of two continuous variables. Multiple linear regression analysis was used with dependent the QoL subscales. Logarithmic transformations of the QoL scales were used for the regression analyses. Internal consistency reliability was determined by the calculation of Cronbach’s α coefficient. All reported p values are two-tailed. Statistical significance was set at p < 0.05 and analyses were conducted using SPSS statistical software (version 27.0). Results: Greater financial toxicity score, i.e., lower toxicity, was significantly associated with greater global health status (rho = 0.34; p < 0.001) and greater physical (rho = 0.37; p < 0.001), role (rho = 0.17; p = 0.001), emotional (rho = 0.34; p < 0.001), cognitive (rho = 0.22; p < 0.001), and social (rho = 0.27; p < 0.001) functioning. Conclusions: There is a strong correlation between a greater financial toxicity score and the quality of life of Greek cancer patients, meaning the lower their financial toxicity, the better their quality of life.

Tree bark plays a protective role by surrounding the wood of a tree like a cloak. Due to its chemical composition and the possibility of its use in various fields, such as pharmaceuticals, landscape architecture, etc., tree bark receives much attention having outstanding importance for industrial utilization and markets. Tree bark is considered a valuable forest product, along with the wood volume. Thus, the accurate prediction of the bark quantity that a tree can produce is of utmost importance for the sustainable management of the forests. For this reason, the knowledge of its quantities, further enables the accurate prediction of the plain wood volume that can be produced by the forest, as well. Because of the nonlinear nature of this biological variable, its accurate quantification is a very complicated problem. Artificial intelligent methods have shown the potential to reliably predict biological variables that are non-linear in nature. In this work, the support vector regression methodology, as a nonlinear nonparametric machine learning approach, is tested for the accurate prediction of the tree bark factor in every different height of the tree bole, through easily obtained measurements on trees.

Background/Objectives: Although the Robin Hood Index (RHI) is increasingly used to quantify geographic health inequality and guide resource redistribution, empirical evidence on whether higher physician density reduces RHI-measured inequality remains limited. This study systematically reviews and meta-analyzes RHI-based research to assess the association between physician distribution and health inequalities. Methods: We conducted a systematic review and meta-analysis of studies using the RHI to evaluate health inequalities, without restrictions on country or publication date. Following PRISMA 2020 guidelines and registered in PROSPERO (CRD42024496486), we searched PubMed, Scopus, and OpenGrey literature, extracted data on physician density and RHI outcomes, and conducted a meta-analysis. Odds ratios (ORs), ln(OR), and 95% confidence intervals (CIs) were calculated, and risk of bias was assessed using the Robvis tool. Results: Seventeen studies covering 720 regions and 1.07 billion individuals were included. Three clusters emerged: physician redistribution (10 studies), poverty–mortality associations (six studies), and systematic reviews (one study). Physician redistribution was strongly associated with increased inequality and policy attention (r = 0.73; p = 0.0038). Meta-analysis of eight redistribution studies yielded a pooled OR of 1.24 (95% CI: 0.54–2.86), consistent in sensitivity analysis (OR = 1.26; 95% CI: 0.56–2.89). Poverty–mortality studies also showed a correlation with the number of variables considered (r = 0.59; p = 0.022). Conclusions: A greater physician supply is associated with increased health inequalities, with statistical support but limited certainty. Methodological heterogeneity in RHI-based studies constrains comparability. Standardized methodologies and broader analytic models are needed to inform research and guide health policy.

Background/Objectives: Nurses are central to artificial intelligence (AI) implementation in healthcare, yet estimates of AI-related anxiety are difficult to compare because Artificial Intelligence Anxiety Scale (AIAS) versions have different total-score ranges. This review estimated AI anxiety using separate syntheses of range-compatible raw scores, with percent-of-maximum-possible (POMP) normalization as a secondary exploratory analysis. Methods: Following PRISMA 2020, PubMed, Scopus, CINAHL, and Google Scholar were searched from inception to 16 July 2026. Methodological quality was appraised using Joanna Briggs Institute checklists. Random-effects models used restricted maximum likelihood estimation with Hartung–Knapp inference; DerSimonian–Laird models were retained for sensitivity analysis. Prediction intervals, leave-one-out analyses, and influence diagnostics were examined. The protocol was retrospectively registered on OSF (DOI: 10.17605/OSF.IO/K3B56). Results: Twenty-five studies included 7271 nurses. The pooled mean was 45.85 (95% CI 44.45–47.26; 95% PI 41.75–49.96; I² = 80.0%) for the 16–80 AIAS (10 studies; N = 2545) and 69.28 (95% CI 62.03–76.53; 95% PI 50.58–87.97; I² = 94.4%) for the 21–147 AIAS (six studies; N = 2066). Pooled means were essentially unchanged under the alternative estimator. Extreme heterogeneity limited interpretation of the exploratory POMP synthesis. Conclusions: Mean AIAS scores were below theoretical midpoints. Raw AIAS totals from incompatible versions should not be pooled. Wide prediction intervals indicate substantial between-study variation, and the absence of validated severity cut-offs precludes clinical severity classification. Longitudinal, controlled, and psychometrically rigorous research is needed before specific anxiety-reduction interventions can be recommended.