Background: Recently, it was shown that the relation between admission glucose and functional outcome after ischemic stroke is described by a J-shaped curve, with a glucose range of 3.7–7.3 mmol/l associated with a favorable outcome. We tested the hypothesis that persistence of hyperglycemia above this threshold at 24–48 h after stroke onset impairs 3-month functional outcome. Methods: We analyzed all patients with glucose >7.3 mmol/l on admission from the Acute STroke Registry and Analysis of Lausanne (ASTRAL). Patients were divided into two groups according to their subacute glucose level at 24–48 h after last well-being time (group 1: ≤7.3 mmol/l, group 2: >7.3 mmol/l). A favorable functional outcome was defined as a modified Rankin Score (mRS) ≤2 at 3 months. A multiple logistic regression analysis of multiple demographic, clinical, laboratory and neuroimaging covariates was performed to assess predictors of an unfavorable outcome. Results: A total of 1,984 patients with ischemic stroke were admitted between January 1, 2003 and October 20, 2009, within 24 h after last well-being time. In the 421 patients (21.2%) with admission glucose >7.3 mmol/l, the proportion of patients with a favorable outcome was not statistically significantly different between the two groups (59.2 vs. 48.7%, respectively). In multiple logistic regression analysis, unfavorable outcome was significantly associated with age (odds ratio, OR: 1.06, 95% confidence interval, 95% CI: 1.03–1.08 for every 10-year increase), National Institute of Health Stroke Score, NIHSS score, on admission (OR: 1.16, 95% CI: 1.11–1.21), prehospital mRS (OR: 12.63, 95% CI: 2.61–61.10 for patients with score >0), antidiabetic drug usage (OR: 0.36, 95% CI: 0.15–0.86) and glucose on admission (OR: 1.16, 95% CI: 1.02–1.31 for every 1 mmol/l increase). No association was found between persistent hyperglycemia at 24–28 h and outcome in either diabetics or nondiabetics. Conclusions: In ischemic stroke patients with acute hyperglycemia, persistent hyperglycemia (>7.3 mmol/l) at 24–48 h after stroke onset is not associated with a worse functional outcome at 3 months whether the patient was previously diabetic or not.

Background/Objectives: Patients’ reports on their satisfaction with the care received often have been seen as a key quality indicator of hospital performance. However, the potential effect of different approaches to its measurement has not been adequately assessed in the health care setting. This study therefore aimed to methodologically compare two different response formats in patient satisfaction questionnaires—Likert scales and Item-Specific Response Options (ISRO)—within a Greek public hospital context. The aim was to comparatively explore resulting item- and scale-level score values, ceiling effects, acquiescence bias, and psychometric properties, including reliability and validity. Methods: An overall sample of 400 hospitalized patients at a National Health Service general university hospital was randomly assigned to two groups during February–March 2025. One group completed a Likert-scale questionnaire and the other a questionnaire, with the same content, that employed an ISRO format instead. The questionnaire items covered two aspects of the hospital experience, these being the satisfaction with doctors/nurses as well as the organization and planning of care. Statistical analysis involved Kolmogorov–Smirnov tests for normality, descriptive statistics, chi-square and Fisher’s exact test, t-tests, Mann–Whitney tests, ceiling effects, regressions, Cronbach’s alpha coefficients, and confirmatory factor analysis (CFA), with measures of composite reliability and average variance extracted and model fit indices. Results: Our analysis identified differences in the distributions of patient responses for many items, including variations in median values and the proportion of positive answers. ISRO items tended to produce higher ratings for nursing care and overall satisfaction, whereas Likert items yielded higher scores in organizational aspects. However, the magnitude of these differences was generally small. Regression analysis, adjusting for length of stay, confirmed statistically significant but modest differences in scale scores between formats. Neither format was superior in terms of ceiling effects, whereas no consistent evidence of acquiescence bias was found. Psychometric testing showed that Likert scales had somewhat higher internal consistency reliability and convergent validity, while ISRO exhibited a better model fit in CFA. Conclusions: The item response format seems to affect reported satisfaction scores, yet the impact is rather limited in practical terms for decision-making. Since neither format is consistently superior, the choice between them should depend on study aims, respondent burden, and the intended use of satisfaction scores by policy makers. Moreover, concerns about acquiescence bias may have been overstated in the health care context. Future research should extend these comparisons with other instruments and larger and more diverse samples, as well as employ complementary methods to clarify how response format affects patient satisfaction measurement.

The aim of this paper is to study the D-optimal $m_1\times m_2\times m_3$, $m_3\ge m_2\ge m_1\ge 2$ saturated factorial designs for estimating the mean and the main effects. A restricted class $\mathcal{D}$ of $m_1\times m_2\times m_3$ saturated designs is defined. In this class, the upper bounds of the determinant values of D-optimal matrices for the already known $2\times m_2\times m_3$, $m_3\ge m_2\ge 2$, $3\times m_2\times m_2$, $m_2\ge 3$, $3\times m_2\times (m_2+1)$, $m_2\ge 3$ and for $4\times 4\times m_3$, $m_3\ge 4$ saturated designs are found. New results regarding the determinant values of D-optimal matrices and the designs achieving them are presented for the $4\times 5\times m_3$, $m_3\ge 5$ and $4\times 6\times m_3$, $m_3\ge 6$ saturated factorial designs in $\mathcal{D}$.

Traditional Operations Research (OR) models incorporate structured numerical inputs, such as demand parameters, costs, emissions, processing times, facility and transport mode capacities, to simulate stochastic behaviors and optimize objective-functions. There is however a vast number of OR-relevant parameters that can be derived from textual and semi-structured sources, such product reviews, social media, news, contracts, procurement documents, ESG reports, policy texts, patents, maintenance records, technical manuals and others. Under this context, the purpose of this study is to develop an artifact and parameter-centered framework for explaining how such textual inputs are transformed into model-ready OR components and incorporated into forecasting, simulation, optimization, logistics, inventory, sustainability, procurement, network analysis, and decision-support models. The main insights derived from the employed framework reveal that: (i) the OR value of textual information lies not in text analysis itself, but in its transformation into validated model-ready artifacts, such as covariates, parameters, constraints, scenarios, rules, weights, graph relations, simulation triggers, and solver inputs; (ii) different textual sources and language-processing methods can generate distinct OR artifacts that enter models through different integration mechanisms, including covariate augmentation, parameter updating, constraint generation, scenario definition, objective-function weighting, graph construction, retrieval support, and solver-code generation; (iii) the same text-derived artifact may play different roles across OR model types, for example a disruption event may update a simulation scenario, increase a lead-time parameter, remove a routing arc, or modify a supplier-risk penalty; and (iv) evaluation must extend beyond NLP accuracy to include artifact validity, parameter validity, model feasibility, mathematical consistency, solver correctness, deployment reliability, and downstream decision usefulness.

3D-printed denture base resins offer significant workflow advantages in denture fabrication, but may exhibit limitations in mechanical performance and surface quality. This in vitro study evaluated the effects of different surface-coating materials on the flexural properties and surface integrity of a 3D-printed denture base resin. Stereolithography-fabricated specimens were airborne-particle abraded and coated with single or double layers of four commercial glaze materials and an unpolymerized denture base resin; untreated specimens served as controls. Flexural properties were assessed utilizing an ISO 20795-1-based design, and surface morphology was evaluated using scanning electron microscopy (SEM) after 90 days of water immersion. Coating application (single versus double layers) did not significantly influence flexural strength or modulus. The control group exhibited the highest mean flexural strength. The unpolymerized resin groups performed closely to the ISO benchmark, whereas commercial coatings failed to enhance mechanical properties, occasionally reducing them below this reference value. Morphological analysis revealed surface irregularities in coatings lacking nanoparticles, whereas nanoparticle-containing coatings showed no visible surface defects after water storage. In conclusion, commercial glazes should be used with caution on 3D-printed resins due to potential mechanical compromise. The application of nanoparticle-containing coatings resulted in a stable surface topography, and unpolymerized resin coatings emerged as a feasible alternative.