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To identify the definitions used for the terms sub-clinical keratoconus and forme fruste keratoconus in published articles. This was a prospective, systematic literature review of the electronic database in PubMed, the Cochrane Library, and LILACS Database of all studies using the keywords \"subclinical keratoconus\" and/or \"forme fruste keratoconus\" until August 18, 2017. Two independent reviewers analyzed the data. The inclusion criteria for articles were having analyzed subclinical keratoconus or forme fruste keratoconus eyes with a sample size greater than 10 eyes; containing the definition of subclinical keratoconus or forme fruste keratoconus; and the quality of published reports was assessed using standards quality index methods. The following aspects of the selected articles were then analyzed: inclusion criteria for definition and technology used. A total of 198 and 95 studies, respectively, including the definition of subclinical keratoconus and forme fruste keratoconus were collected in an initial search, of which 165 and 73 studies, respectively, were excluded. Definitions for subclinical keratoconus and forme fruste keratoconus included the criteria of having keratoconus in the fellow eye in 72.72% (24 of 33) and 77.27% (17 of 22) of the articles, respectively. A total of 96.97% (32 of 33) and 90.90% (20 of 22) of the studies used more than one parameter to define subclinical keratoconus and forme fruste keratoconus, respectively. The most common extra parameters included normal slit-lamp examination and cornea on slit-lamp biomicroscopy and inferior-superior asymmetry and/or bowtie pattern with skewed radial axes. This review demonstrates the lack of unified criteria to define subclinical keratoconus and forme fruste keratoconus. According to the literature review, the most common subclinical keratoconus definition used refers to an eye with topographic signs of keratoconus and/or suspicious topographic findings under normal slit-lamp examination and keratoconus in the fellow eye and the most common forme fruste keratoconus definition refers to an eye with normal topography, normal slit-lamp examination, and keratoconus in the fellow eye. [J Refract Surg. 2020;36(4):270-279.].

Energy communities enable prosumers to jointly operate distributed energy resources and thereby generate economic benefits that exceed those achievable individually. A central challenge in their implementation is selecting a Cost Allocation Method (CAM) that distributes these benefits fairly among heterogeneous participants. Although numerous CAMs have been proposed, they are often evaluated under different assumptions, making direct comparison difficult. This paper develops a unified axiomatic framework for assessing CAMs in energy communities and applies it to eight representative methods classified in three families: simple rules, savings-based, and price-based. The framework is built around seven desirable properties capturing principles of fairness, environmental friendliness, and continuity. Our main contribution is a comparative table that positions all methods within a single evaluative space and reveals the structural trade-offs that arise across CAMs. The analysis shows that the Average-Price CAM satisfies the same axiomatic properties as the Shapley method while remaining computationally trivial, making it an attractive practical option. We also show that the Extreme-Price CAM is the only price-based method that ensures the property of Beneficial Group Participation (core stability); however, this method violates other properties related to environmental friendliness and continuity—trade-offs we prove to be unavoidable for price-based rules. Finally, we conjecture that the nucleolus satisfies all seven properties, although its computation is rarely feasible in practice. The proposed framework provides researchers and practitioners with a transparent foundation for selecting and designing cost allocation methods in emerging energy communities.

Energy Communities (ECs) have emerged as a key instrument for promoting local renewable energy integration and citizen participation in the energy transition. While their economic performance largely depends on the ability to generate savings through self-consumption and internal energy trading, their long-term viability is strongly influenced by how these savings are distributed among heterogeneous participants. Despite extensive literature on cost allocation methods, there remains a lack of integrated, data-driven approaches that clearly disentangle the sources of savings in ECs and examine how different allocation methods perform under realistic operating conditions. This paper presents a simulation-based analytical framework to quantify the economic savings generated within Energy Communities and to analyse how a set of widely used cost allocation methods distribute these savings among participants. The approach explicitly separates savings due to renewable self-consumption from those arising from internal trading in a Local Energy Market and explores allocation outcomes across a broad range of community configurations. Extensive simulations based on both synthetic and real-world consumption and price data are used to examine community-level savings, individual outcomes, surplus distribution patterns between Net Consumers and Net Producers, and computational tractability. The results show that internal energy trading consistently increases community-level savings, although its contribution is typically modest relative to self-consumption and strongly dependent on contextual factors such as renewable penetration, demand heterogeneity, and price conditions. The analysis highlights important trade-offs between savings generation, surplus distribution, and computational feasibility, underscoring the relevance of context-aware selection of allocation mechanisms. Overall, the proposed approach provides a transparent and reproducible tool for analysing the economic performance of Energy Communities under practical constraints.

Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to explore the clinical characteristics and predictive factors of patients with COVID-19. Our primary objectives are to describe the clinical characteristics and determine the factors that predict intensive care unit (ICU) admission of patients with COVID-19. Determining these factors using a well-defined population can increase our understanding of the real-world epidemiology of the disease. We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling) to analyze the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the Servicio de Salud de Castilla-La Mancha (SESCAM) Health Care Network (Castilla-La Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1 to March 29, 2020. We extracted related clinical information regarding diagnosis, progression, and outcome for all COVID-19 cases. A total of 10,504 patients with a clinical or polymerase chain reaction-confirmed diagnosis of COVID-19 were identified; 5519 (52.5%) were male, with a mean age of 58.2 years (SD 19.7). Upon admission, the most common symptoms were cough, fever, and dyspnea; however, all three symptoms occurred in fewer than half of the cases. Overall, 6.1% (83/1353) of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, without tachypnea, and temperature <39 degrees Celsius (or >39 ºC without respiratory crackles) were not admitted to the ICU. In contrast, patients with COVID-19 aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnea and delayed their visit to the emergency department after being seen in primary care. Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnea with or without respiratory crackles) predicts whether patients with COVID-19 will require ICU admission.

We evaluate the long-term visual, refractive, and keratometric outcomes after corneal crosslinking (CXL) in patients with progressive keratoconus (KC) and the incidence of an extreme corneal flattening effect. Oftalmosalud Institute of Eyes, Lima, Perú. Retrospective cohort study. Forty-five eyes that underwent CXL with epithelial removal between June 2006 and September 2011. Data analysis was performed at preoperative evaluation, 1 year postoperatively, and at least 10 years or more postoperatively. Outcome measures included uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), and Scheimpflug (Pentacam) analysis. Progression was defined by an increase in steep keratometry (Ks) of 1.5D or greater between 2 examinations. Extreme flattening effect was defined as a decrease in K values equal to or greater than 5 diopters (D). Mean follow-up time was 11 ± 1.07 years (range 10-13 years). There was a significant improvement in Ks, UCVA, CDVA, and spherical equivalent at the last visit. The overall rate of progression was 2.22% (1/45). Extreme flattening was observed in 15.5% (7/45) of the eyes, and this was associated with a loss of CDVA in 4.44% (2/45) of the eyes. One eye with corneal flattening of 11.5 D lost 7 lines of CDVA and required corneal transplantation. CXL is a safe and effective procedure to stop the progression of KC with a good overall long-term success rate. Extreme corneal flattening may be more common than commonly recognized, and severe corneal flattening associated with a decrease in CDVA may occur.

Patients with chronic obstructive pulmonary disease (COPD) have few options for treatment. The efficacy and safety of the phosphodiesterase-4 inhibitor roflumilast have been investigated in studies of patients with moderate-to-severe COPD, but not in those concomitantly treated with longacting inhaled bronchodilators. The effect of roflumilast on lung function in patients with COPD that is moderate to severe who are already being treated with salmeterol or tiotropium was investigated. In two double-blind, multicentre studies done in an outpatient setting, after a 4-week run-in, patients older than 40 years with moderate-to-severe COPD were randomly assigned to oral roflumilast 500 μg or placebo once a day for 24 weeks, in addition to salmeterol (M2-127 study) or tiotropium (M2-128 study). The primary endpoint was change in prebronchodilator forced expiratory volume in 1 s (FEV 1). Analysis was by intention to treat. The studies are registered with ClinicalTrials.gov, number NCT00313209 for M2-127, and NCT00424268 for M2-128. In the salmeterol plus roflumilast trial, 466 patients were assigned to and treated with roflumilast and 467 with placebo; in the tiotropium plus roflumilast trial, 371 patients were assigned to and treated with roflumilast and 372 with placebo. Compared with placebo, roflumilast consistently improved mean prebronchodilator FEV 1 by 49 mL (p<0·0001) in patients treated with salmeterol, and 80 mL (p<0·0001) in those treated with tiotropium. Similar improvement in postbronchodilator FEV 1 was noted in both groups. Furthermore, roflumilast had beneficial effects on other lung function measurements and on selected patient-reported outcomes in both groups. Nausea, diarrhoea, weight loss, and, to a lesser extent, headache were more frequent in patients in the roflumilast groups. These adverse events were associated with increased patient withdrawal. Roflumilast improves lung function in patients with COPD treated with salmeterol or tiotropium, and could become an important treatment for these patients. Nycomed.

The use of inhaled corticosteroids (ICS) in combination with bronchodilators in patients with COPD has been shown to decrease the rate of disease exacerbations and to improve the lung function and patients' quality of life. However, their use has also been associated with an increased risk of pneumonia. We have reviewed existing clinical evidence on the risks and benefits of ICS in COPD, including large randomized clinical trials, meta-analyses, and clinical reviews. A large body of evidence supports the clinical benefits of ICS in patients with COPD in terms of exacerbations, symptoms, lung function, and quality of life. The incidence of adverse events related to ICS, including pneumonia, varies strongly among the studies and seems to be dose dependent, with recent well-designed, large studies on low-dose ICS reporting similar safety profiles in ICS and non-ICS groups. The benefits of ICS in COPD continue to outweigh the risks, especially when lower ICS doses are employed. Given that the data on ICS withdrawal in COPD are scarce and conflicting, we argue that using reduced doses of ICS could be an optimal strategy to manage patients with COPD.

Accurate detection of post-refractive ectasia susceptibility is essential during preoperative evaluation for laser vision correction (LVC) due to the risk of progressive corneal ectasia and vision decline post-surgery. Despite improved screening and a reduced incidence from 0.66 to 0.033%, iatrogenic ectasia remains a concern due to the severe vision loss it can cause, highlighting the need for more accurate detection tools. A new optimized version of the Belin/Ambrósio Enhanced Ectasia Display version 4 (BAD-D v4) was developed and validated across 26 international centers to enhance the detection of keratoconus and very asymmetric ectasia and to assess the risk of post-refractive ectasia. Analyzing a dataset of 3,886 eyes from 3,351 patients, including normal, keratoconus (KC), and cases with very asymmetric ectasia (VAE) categories, having one eye with normal topography (VAE-NT and the fellow eye with clinical ectasia (VAE-E). The study utilized an optimized logistic regression algorithm improving diagnostic accuracy. The BAD-D v4 showed superior efficacy in differentiating normal eyes from ectatic conditions, with Area Under the Receiver Operating Characteristic Curve (AUROC) scores of 0.997 and 0.998 in training and testing samples for normal versus clinical ectasia. Additionally, in Normal vs. Disease (KC + VAE), the AUROC was 0.974 and 0.966, and in the challenging Normal vs. VAE-NT diverse group, it scored 0.905 and 0.858. These results outperformed the current version (BAD-D v3) and were comparable to the Pentacam Random Forest Index in all tested scenarios, highlighting the potential of BAD-D v4 in early ectasia detection, without altering the index scale or the end-user experience.

The application of machine learning (ML) algorithms to address problems related to model-driven engineering (MDE) is currently hindered by the lack of curated datasets of software models. There are several reasons for this, including the lack of large collections of good quality models, the difficulty to label models due to the required domain expertise, and the relative immaturity of the application of ML to MDE. In this work, we present ModelSet, a labelled dataset of software models intended to enable the application of ML to address software modelling problems. To create it we have devised a method designed to facilitate the exploration and labelling of model datasets by interactively grouping similar models using off-the-shelf technologies like a search engine. We have built an Eclipse plug-in to support the labelling process, which we have used to label 5,466 Ecore meta-models and 5,120 UML models with its category as the main label plus additional secondary labels of interest. We have evaluated the ability of our labelling method to create meaningful groups of models in order to speed up the process, improving the effectiveness of classical clustering methods. We showcase the usefulness of the dataset by applying it in a real scenario: enhancing the MAR search engine. We use ModelSet to train models able to infer useful metadata to navigate search results. The dataset and the tooling are available at https://figshare.com/s/5a6c02fa8ed20782935c and a live version at http://modelset.github.io.

In the search for solutions to national skill formation challenges, the central and northern Europeans models of good practices in dual vocational education and training (VET) permeated the imaginary of the southern countries. Particularly, the German dual VET system has been predominant in the discourse of policy-makers. This research analyses the role played by the European Union (EU) in the promotion of this model as the reference for the reforming Member States during the policy transfer stage of cross-national attraction. This paper develops a reflexive Thematic Analysis among the fundamental documentation related to VET resulting from the EU Governance Triangle. Its aim is to interpret the EU proposal for the reform of VET, with a focus on the characteristics which shape the way in which the training is developed. This Thematic Analysis reveals that the EU promotes a VET training model based in work-based learning as a pillar, preferably articulated in the form of apprenticeships. This, along with the general principles for the regulation of apprenticeships promoted by the EU, proves the agency role exercised by the EU in the dissemination of dual VET as a model of good practice.