Abstract

Abstract
Health system performance assessment (HSPA) is essential for health planning and to improve population health. One of the HSPA domains is related to effectiveness, which can be represented considering different dimensions. Composite indicators can be used to summarize complex constructs involving several indicators. One example of such efforts is the Healthcare Access and Quality Index from the Global Burden of Diseases Study, in which different causes of mortality amenable to health care are summarized in this index through principal component analysis and exploratory factor analysis. While these approaches use the variance of the indicators, marginal improvement is not considered, that is, the distance to the best practice frontier. In this study we propose an innovative benefit-of-the-doubt approach to combine frontier analysis and composite indicators, using amenable mortality estimates for 188 countries. In particular, we include flexible aggregating weighting schemes and a robust and conditional approach. The dual formulation gives information on the peers and the potential mortality rate reduction targets considering the background conditions. In absolute terms, Andorra and high-income countries are the most effective regarding healthcare access and quality, while sub-Saharan African and South Asian countries are the least effective. North African and Middle Eastern countries benefit the most when epidemiological patterns, geographical proximity, and country development status are considered.

Abstract

Abstract
Background This scoping review systematically maps externally validated machine learning (ML)-based models in cancer patient care, quantifying their performance, and clinical utility, and examining relationships between models, cancer types, and clinical decisions. By synthesizing evidence, this study identifies, strengths, limitations, and areas requiring further research. Methods The review followed the Joanna Briggs Institute's methodology, Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, and the Population, Concept, and Context mnemonic. Searches were conducted across Embase, IEEE Xplore, PubMed, Scopus, and Web of Science (January 2014-September 2022), targeting English-language quantitative studies in Q1 journals (SciMago Journal and Country Ranking > 1) that used ML to evaluate clinical outcomes for human cancer patients with commonly available data. Eligible models required external validation, clinical utility assessment, and performance metric reporting. Studies involving genetics, synthetic patients, plants, or animals were excluded. Results were presented in tabular, graphical, and descriptive form. Results From 4023 deduplicated abstracts and 636 full-text reviews, 56 studies (2018-2022) met the inclusion criteria, covering diverse cancer types and applications. Convolutional neural networks were most prevalent, demonstrating high performance, followed by gradient- and decision tree-based algorithms. Other algorithms, though underrepresented, showed promise. Lung and digestive system cancers were most frequently studied, focusing on diagnosis and outcome predictions. Most studies were retrospective and multi-institutional, primarily using image-based data, followed by text-based and hybrid approaches. Clinical utility assessments involved 499 clinicians and 12 tools, indicating improved clinician performance with AI assistance and superior performance to standard clinical systems. Discussion Interest in ML-based clinical decision-making has grown in recent years alongside increased multi-institutional collaboration. However, small sample sizes likely impacted data quality and generalizability. Persistent challenges include limited international validation across ethnicities, inconsistent data sharing, disparities in validation metrics, and insufficient calibration reporting, hindering model comparison reliability.

Abstract
This paper presents the development process of the mobile App D.R.E.A.M., Design-thinking to Reach-out, Embrace and Acknowledge Mental health, which is a tool for self-assessment and self-care in promoting the mental health of higher education students. In Portugal, the program for promoting Mental Health in higher education advocates the development and use of digital tools, such as apps and/or social networks and platforms, aimed at promoting wellbeing and with the potential for use to be more accessible to higher education students. The objective of this app is to promote the mental health and wellbeing of higher education students. Design Thinking was used as the methodology for building the app, which was developed using a combination of low-code/no-code tools, Flutter/Dart coding, and Google's Firebase capabilities and database functionalities. In the first semester of the 2023/2024 academic year, 484 students downloaded the app, and 22 emails were received for psychological consultations. A dynamic update of the app is required, with modules on time management and study organization, structured physical activity programs, development of socio-entrepreneurial skills, and vocational area.

Abstract
The extraction of phenolic compounds from eucalyptus leaves was optimized using heat and ultrasound-assisted techniques, and the bioactive potential of the resulting extract was assessed. The independent variables, including time (t), solvent concentration (S), and temperature (T) or power (P), were incorporated into a five- level central composite design combined with Response Surface Methodology. Phenolic content was determined by HPLC-DAD-ESI/MS and used as response criteria. The developed models were successfully fitted to the experimental data to identify the optimal extraction conditions. Heat-assisted extraction proved to be the most efficient method for phenolic recovery, yielding 27 +/- 2 mg/g extract under optimal conditions (120 min, 76.5 degrees C, and 25 % ethanol, v/v). The extracts exhibited a high concentration of phenolic glycoside derivatives, including gallotannin, quercetin, and isorhamnetin. These findings suggest that the extracts hold promise as natural additives in food technology, owing to their moderate antimicrobial activity and strong antioxidant properties.