Search Results (8)

Background/Objectives: Pediatric shock is a final common pathway of cardiovascular failure across diverse emergencies, yet data from mixed emergency cohorts outside intensive care units remain limited. This study aimed to describe the distribution, etiologic subtypes, and clinical correlates of shock in children presenting within a diagnosis-based emergency cohort. Methods: A retrospective single-centre study was conducted in children aged 0–16 years presenting with selected acute pediatric emergencies, among whom cases with and without shock were compared. Shock was defined using documented diagnoses and compatible hemodynamic features, and multiple etiologic types of shock were analyzed, including hypovolemic, septic, cardiogenic, and anaphylactic shock. Demographic and diagnostic variables—age, length of stay, organ support, age strata, and selected comorbidities—and baseline clinical features were compared between children with and without shock using non-parametric and χ²/Fisher’s exact tests. Results: Within the prespecified diagnosis-based analytic cohort, 36/128 children (28.1%) met the study criteria for shock and occurred across all prespecified acute pediatric emergency groups, with the highest proportional burden in heart failure and meningitis; this proportion should not be interpreted as an emergency-department prevalence estimate. Children with shock were younger, with clustering in infants < 1 year and those aged 5–9 years, and tended to stay longer in hospital. Pre-existing cardiac disease, severe dehydration, and altered mental status/coma were more frequent among children with shock. Septic and cardiogenic shock required the most intensive organ support. Conclusions: In this pediatric emergency cohort, shock emerged as a clinically relevant and etiologically heterogeneous complication across diverse acute presentations, with a distinct age-related vulnerability pattern and consistent associations with readily identifiable bedside clinical features. Simple bedside information—particularly cardiac comorbidity, dehydration, and altered consciousness—may assist the early recognition of children with evolving circulatory failure and support closer monitoring and timely escalation of care. By focusing on a mixed emergency population outside the intensive care unit, this study provides a real-world clinical perspective that may help refine early bedside assessment and improve vigilance for shock in pediatric emergency departments. Full article

Background and Objectives: Tear film instability and corneal surface irregularity are important sources of variability in keratometric and corneal topographic measurements, particularly affecting astigmatic magnitude and axis. Accurate preoperative biometry is crucial for optimal refractive outcomes in cataract surgery. Dry eye disease (DED) may compromise the reproducibility of keratometric parameters, leading to errors in intraocular lens (IOL) power calculation. This study aimed to evaluate the impact of DED on the reproducibility of keratometric measurements and to assess the effect of a four-week treatment with lipid-containing artificial tears on these parameters in cataract patients. Materials and Methods: This cross-sectional study included 116 patients scheduled for cataract surgery, of whom 65 (56.0%) had DED and 51 (44.0%) served as controls. All patients underwent two preoperative keratometric measurements 10–20 min apart (IOL1 and IOL2). The control group proceeded to surgery the next day, while surgery in the DED group was postponed. Patients with DED received preoperative therapy with lipid-containing artificial tears. Follow-up assessments occurred one month after therapy (keratometric measurement named IOL3) and eight weeks postoperatively. Clinical evaluation included slit-lamp examination, dry eye testing according to Dry eye Workshop II (DEWS II) criteria: Ocular surface Disease Index (OSDI), Tear Break-Up Time (TBUT), Schirmer I, Oxford staining, and meibomian gland assessment), ocular biometry, and postoperative spherical equivalent measurement using an auto ref-keratometer. Nonparametric statistical analyses were applied to evaluate associations between parameters. Results: In the DED group, corneal astigmatism showed a significant difference between IOL1 and IOL2 (Wilcoxon signed-rank test {Z = 2.43; p = 0.015}). Significant changes in predicted IOL power were observed between pretreatment and posttreatment values (t = 2.57; p = 0.013) and between IOL2 and IOL3 (t = 2.23; p = 0.029), indicating improved keratometric stability following tear film therapy. No additional significant correlations were identified. Conclusions: DED adversely affects the reproducibility of keratometric measurements and may compromise IOL power selection. Preoperative identification and treatment of DED, followed by repeated biometry after tear film stabilization, are strongly recommended to enhance refractive accuracy and optimize surgical outcomes in cataract patients. Full article

The article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density estimation, projection pursuit, log-spline estimation, and wavelet-based estimation. The study is extended with modified versions of these methods, where the sample is first clustered using the EM algorithm based on Gaussian mixture components prior to density estimation. Estimation accuracy is quantitatively evaluated using MAE and MAPE criteria, with simulation experiments conducted over 100,000 replications for various sample sizes. The results show that estimation accuracy strongly depends on the density structure, sample size, and degree of component overlap. Clustering before density estimation significantly improves accuracy for multimodal and asymmetric densities. Although no formal statistical tests are conducted, the performance improvement is validated through non-overlapping confidence intervals obtained from 100,000 simulation replications. In addition, several decision-making systems are compared for automatically selecting the most appropriate estimation method based on the sample’s statistical features. Among the tested systems, kernel discriminant analysis yielded the lowest error rates, while neural networks and hybrid methods showed competitive but more variable performance depending on the evaluation criterion. The findings highlight the importance of using structurally adaptive estimators and automation of method selection in nonparametric statistics. The article concludes with recommendations for method selection based on sample characteristics and outlines future research directions, including extensions to multivariate settings and real-time decision-making systems. Full article

Introduction: Osseodensification is a non-extraction technique using specially designed drills to increase bone density while extending an osteotomy, allowing bone to be preserved and condensed by compacting autograft during osteotomy preparation, increasing bone density around implants, and improving mechanical stability. Aim: The objective of this study is to compare conventional osteotomy and osseodensification protocols in implant placement and analyze whether there are differences in bone density. Materials and Methods: Study variables were defined, namely, osseodensification technique, conventional osteotomy technique, bone density, sex, area of location, implant dimensions, implant dimensions, and implant stability. Eligibility and exclusion criteria were defined. A step-by-step surgical protocol was developed. The surgeon and radiologist underwent intra-examiner calibration. A total of 15 patients were selected according to the eligibility criteria, and a total of 41 implants were inserted, 20 implants by conventional osteotomy and 21 by osseodensification. A cone beam computed tomography was performed one year after prosthetic rehabilitation to estimate bone density. Data were collected and recorded, and in the analysis of the association of variables, non-parametric tests were applied. Results: Significant statistical results were found in bone density values, with higher values being obtained with the osseodensification technique, that is, median density values of 1020, and median density values of 732 for the bone drilling technique. The results of the correlation between bone density in both techniques and sex, primary implant stability, implant dimensions and location area were statistically non-significant. Conclusions: Within the limitations of this study, there are differences in bone density between conventional osteotomy and osseodensification protocols. Bone density is increased with osseodensification over a study period of one year. Full article

Background: This study addresses the significant challenge of low survival rates in patients with cause-specific lung cancer accompanied by bone or brain metastases. Recognizing the critical need for an effective predictive model, the research aims to establish survival prediction models using both parametric and non-parametric approaches. Methods: Clinical data from lung cancer patients with at least one bone or brain metastasis between 2000 and 2020 from the SEER database were utilized. Four models were constructed: Cox proportional hazard, Weibull accelerated failure time (AFT), log-normal AFT, and Zografos–Balakrishnan log-normal (ZBLN). Independent prognostic factors for cause-specific survival were identified, and model fit was evaluated using Akaike’s and Bayesian information criteria. Internal validation assessed predictive accuracy and discriminability through the Harriel Concordance Index (C-index) and calibration plots. Results: A total of 20,412 patients were included, with 14,290 (70%) as the training cohort and 6122 (30%) validation. Independent prognostic factors selected for the study were age, race, sex, primary tumor site, disease grade, total malignant tumor in situ, metastases, treatment modality, and histology. Among the accelerated failure time (AFT) models considered, the ZBLN distribution exhibited the most robust model fit for the 3- and 5-year survival, as evidenced by the lowest values of Akaike’s information criterion of 6322 and 79,396, and the Bayesian information criterion of 63,495 and 79,396, respectively. This outperformed other AFT and Cox models (AIC = [156,891, 211,125]; BIC = [158,848, 211,287]). Regarding predictive accuracy, the ZBLN AFT model achieved the highest concordance C-index (0.682, 0.667), a better performance than the Cox model (0.669, 0.643). The calibration curves of the ZBLN AFT model demonstrated a high degree of concordance between actual and predicted values. All variables considered in this study demonstrated significance at the 0.05 level for the ZBLN AFT model. However, differences emerged in the significant variations in survival times between subgroups. The study revealed that patients with only bone metastases have a higher chance of survival compared to only brain and those with bone and brain metastases. Conclusions: The study highlights the underutilized but accurate nature of the accelerated failure time model in predicting lung cancer survival and identifying prognostic factors. These findings have implications for individualized clinical decisions, indicating the potential for screening and professional care of lung cancer patients with at least one bone or brain metastasis in the future. Full article

Despite the impact that climate change is having on our planet and considering its consequences for future generations, much of the academic literature focuses on adolescent and adult perceptions, giving little relevance to children’s perceptions. Children’s voices have the potential to influence public opinion, which may in turn determine the direction of a new policy on the climate crisis. In this context, it is urgent that we understand how children perceive this problem. This quantitative study was based on the application of 245 questionnaires to children aged between 9 and 13 years old from five schools in north-eastern Portugal, more specifically in the region of Trás-os-Montes. We can say that this study was a convenience study because we delivered the surveys in the schools closest to the working area of the researchers. We used a questionnaire with 26 questions, 24 of which had closed responses (like the Likert type), one open response, and one with multiple choices. In this work, we conducted a descriptive and inferential statistical analysis, and prepared a database, using the statistical software IBM SPSS, which allowed us to conduct some statistical tests, selected according to variables. For the descriptive analysis, several parameters were used for the distribution of variables, namely, frequency, percentage, mean, and standard deviation. We rejected the null hypothesis (H0) and assumed for the inferential analysis that the sample does not follow a normal distribution, considering the fulfillment of the necessary criteria for parametric tests and after performing the Kolmogorov–Smirnov normality test, whose null hypothesis (H0) is that data are normally distributed, and given that the p-value for the variables under study was p < 0.05. In this regard, non-parametric tests were used. The Mann–Whitney test was used to compare the degree of agreement with climate change statements as a function of the student’s gender and year of schooling, which is a non-parametric test suitable for comparing the distribution functions of an ordinal variable measured in two independent samples. The results show that most of the children expressed concern about the study’s potential problem, and (42%) said they are concerned about climate change. However, they show some doubts and a lack of knowledge about some of the themes, like (33.5%) cannot name only one consequence of climate change. We also found differences between the two study cycles, with children in the 6th grade having a higher average in their understanding of the phenomenon (p = 0.049), as well as the level of education of the parents being positively correlated with a more ecocentric posture, we can see this when we considering the variable parents. We also found that 46.6% of the students say that television is where they learn more about climate change. From the results obtained, we can open new paths for future research and contribute to the definition of policies and educational practices since the school has the responsibility to cooperate in the production of values, attitudes, and pro-environmental behaviors. Full article

Purpose: To analyze the vaccine effect by comparing five groups: unvaccinated patients with Alpha variant, unvaccinated patients with Delta variant, vaccinated patients with Delta variant, unvaccinated patients with Omicron variant, and vaccinated patients with Omicron variant, assessing the “gravity” of COVID-19 pulmonary involvement, based on CT findings in critically ill patients admitted to Intensive Care Unit (ICU). Methods: Patients were selected by ICU database considering the period from December 2021 to 23 March 2022, according to the following inclusion criteria: patients with proven Omicron variant COVID-19 infection with known COVID-19 vaccination with at least two doses and with chest Computed Tomography (CT) study during ICU hospitalization. Wee also evaluated the ICU database considering the period from March 2020 to December 2021, to select unvaccinated consecutive patients with Alpha variant, subjected to CT study, consecutive unvaccinated and vaccinated patients with Delta variant, subjected to CT study, and, consecutive unvaccinated patients with Omicron variant, subjected to CT study. CT images were evaluated qualitatively using a severity score scale of 5 levels (none involvement, mild: ≤25% of involvement, moderate: 26–50% of involvement, severe: 51–75% of involvement, and critical involvement: 76–100%) and quantitatively, using the Philips IntelliSpace Portal clinical application CT COPD computer tool. For each patient the lung volumetry was performed identifying the percentage value of aerated residual lung volume. Non-parametric tests for continuous and categorical variables were performed to assess statistically significant differences among groups. Results: The patient study group was composed of 13 vaccinated patients affected by the Omicron variant (Omicron V). As control groups we identified: 20 unvaccinated patients with Alpha variant (Alpha NV); 20 unvaccinated patients with Delta variant (Delta NV); 18 vaccinated patients with Delta variant (Delta V); and 20 unvaccinated patients affected by the Omicron variant (Omicron NV). No differences between the groups under examination were found (p value > 0.05 at Chi square test) in terms of risk factors (age, cardiovascular diseases, diabetes, immunosuppression, chronic kidney, cardiac, pulmonary, neurologic, and liver disease, etc.). A different median value of aerated residual lung volume was observed in the Delta variant groups: median value of aerated residual lung volume was 46.70% in unvaccinated patients compared to 67.10% in vaccinated patients. In addition, in patients with Delta variant every other extracted volume by automatic tool showed a statistically significant difference between vaccinated and unvaccinated group. Statistically significant differences were observed for each extracted volume by automatic tool between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant of COVID-19. Good statistically significant correlations among volumes extracted by automatic tool for each lung lobe and overall radiological severity score were obtained (ICC range 0.71–0.86). GGO was the main sign of COVID-19 lesions on CT images found in 87 of the 91 (95.6%) patients. No statistically significant differences were observed in CT findings (ground glass opacities (GGO), consolidation or crazy paving sign) among patient groups. Conclusion: In our study, we showed that in critically ill patients no difference were observed in terms of severity of disease or exitus, between unvaccinated and vaccinated patients. The only statistically significant differences were observed, with regard to the severity of COVID-19 pulmonary parenchymal involvement, between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant, and between unvaccinated patients with Delta variant and vaccinated patients with Delta variant. Full article

We consider a nonparametric Generative Tree Model and discuss a problem of selecting active predictors for the response in such scenario. We investigated two popular information-based selection criteria: Conditional Infomax Feature Extraction (CIFE) and Joint Mutual information (JMI), which are both derived as approximations of Conditional Mutual Information (CMI) criterion. We show that both criteria CIFE and JMI may exhibit different behavior from CMI, resulting in different orders in which predictors are chosen in variable selection process. Explicit formulae for CMI and its two approximations in the generative tree model are obtained. As a byproduct, we establish expressions for an entropy of a multivariate gaussian mixture and its mutual information with mixing distribution. Full article