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Engineering profession for students and diverse students for Engineering Campuses (ECs) is the prestige to have for both. Worldwide higher education has been impacted by COVID-19 pandemic, but particularly pulling padlocked doors of Indian engineering campuses (ECs) down. Students’ attitudes regarding choice, liking, and preferences were also affected. Knowing how tough ’college choice’ was before the pandemic, one can guess how difficult it will be today. The objective of this study was to explore students’ perceptions of choice characteristics related to ECs and diverse students enabling choice decisions under the COVID-19 situation, and to discover any possible relationships among them. Research questions were qualitatively examined with the statistical confirmation of related hypothesizes by utilizing ANOVA and Regression analysis. A self-reported quantitative survey composed of a closed-ended structured questionnaire was administered on the students of first-year engineering who had recently enrolled in ECs of North Maharashtra Region of India, after pandemic hitting India. According to the study, ECs have several characteristics impacting students’ selection of ECs under pandemic. The influence of proximity, image and reputation, educational quality, and curriculum delivery was significant in contributing sustainability of ECs. This influence was significant across students’ psychological and behavioural biases on likes, choices, and preferences. Furthermore, multiple relationships were noted within the sub-groups of demographic, geographic, socioeconomic, academic performance, and psychological and behavioural traits due to the impact of ECs’ characteristics on sustainability. The study has provided a framework for policymakers and administrators to strengthen repositioning towards sustainability while capturing potentially diverse enrolments. Even if we have to coexist with pandemic forever or with more similar pandemics, the findings of this study may undergo a fundamental transformation for ECs (existing and forthcoming). On the other hand, by understanding the importance and relations of choice characteristics may smoothen the complex nature of \"college choice\" for prospective students.

[...]several studies have demonstrated that the indiscriminate administration of antimicrobial therapy to children can contribute to antimicrobial resistance [8]. [...]it is imperative that antimicrobial therapy be tailored to the child with high risk of a bacterial pathogen and potentially avoided if a child has low risk of a bacterial infection. [...]the crucial question in the management of LRTI in children in low- and middle-income countries is: Ideally, biomarker levels used for clinical decision-making should be empirically derived against the outcome of interest. [...]studies are needed to elucidate a CRP level at which antimicrobial therapy can be withheld without negative outcomes for children with LRTI. [...]it is important to note that this study was limited to children with mild LRTI.

Acute gastroenteritis is a common illness, and treatment with probiotics is common. In a double-blind, placebo-controlled trial, treatment with Lactobacillus rhamnosus GG was found to afford no benefit in reducing symptoms associated with acute gastroenteritis in children.

While the literature documenting health disparities has advanced in recent decades, less is known about the pattern of racial/ethnic disparities in emergency care in the United States. To describe the trends and differences of health outcomes and resource utilization among racial/ethnic groups in US emergency care for adult patients over a 12-year period. This cross-sectional study of emergency department (ED) data from the nationally representative National Hospital Ambulatory Medical Survey (NHAMCS) examined multiple dimensions of ED care and treatment from 2005 to 2016 among adults in the US. The main outcomes include ED care outcomes (hospital admission, ICU admission, and death in the ED/hospital), resource utilization outcomes (medical imaging use, blood test, and procedure use), and patients' waiting time in the ED. The main exposure variable is race/ethnicity including white patients (non-Hispanic), black patients (non-Hispanic), Hispanic patients, Asian patients, and Other. During the 12-year study period, NHAMCS collected data on 247,989 adult (> 18 years old) ED encounters, providing a weighted sample of 1,065,936,835 ED visits for analysis. Asian patients were 1.21 times more likely than white patients to be admitted to the hospital following an ED visit (aOR 1.21, 95% CI 1.12-1.31). Hispanic patients presented no significant difference in hospital admission following an ED visit (aOR 1.01, 95% CI 0.97-1.06) with white patients. Black patients were 7% less likely to receive an urgent ESI score than white patients less likely to receive immediate or emergent scores, as opposed to semi- or non-urgent scores. Black patients were also 10% less likely than white patients to be admitted to the hospital and were 1.26 times more likely than white patients to die in the ED or hospital. Race is associated with significant differences in ED treatment and admission rates, which may represent disparities in emergency care. Hispanic and Asian Americans were equal or more likely to be admitted to the hospital compared to white patients. Black patients received lower triage scores and higher mortality rates. Further research is needed to understand the underlying causes and long-term health consequences of these disparities.

Background Early screening and accurately identifying Acute Appendicitis (AA) among patients with undifferentiated symptoms associated with appendicitis during their emergency visit will improve patient safety and health care quality. The aim of the study was to compare models that predict AA among patients with undifferentiated symptoms at emergency visits using both structured data and free-text data from a national survey. Methods We performed a secondary data analysis on the 2005-2017 United States National Hospital Ambulatory Medical Care Survey (NHAMCS) data to estimate the association between emergency department (ED) patients with the diagnosis of AA, and the demographic and clinical factors present at ED visits during a patient’s ED stay. We used binary logistic regression (LR) and random forest (RF) models incorporating natural language processing (NLP) to predict AA diagnosis among patients with undifferentiated symptoms. Results Among the 40,441 ED patients with assigned International Classification of Diseases (ICD) codes of AA and appendicitis-related symptoms between 2005 and 2017, 655 adults (2.3%) and 256 children (2.2%) had AA. For the LR model identifying AA diagnosis among adult ED patients, the c-statistic was 0.72 (95% CI: 0.69–0.75) for structured variables only, 0.72 (95% CI: 0.69–0.75) for unstructured variables only, and 0.78 (95% CI: 0.76–0.80) when including both structured and unstructured variables. For the LR model identifying AA diagnosis among pediatric ED patients, the c-statistic was 0.84 (95% CI: 0.79–0.89) for including structured variables only, 0.78 (95% CI: 0.72–0.84) for unstructured variables, and 0.87 (95% CI: 0.83–0.91) when including both structured and unstructured variables. The RF method showed similar c-statistic to the corresponding LR model. Conclusions We developed predictive models that can predict the AA diagnosis for adult and pediatric ED patients, and the predictive accuracy was improved with the inclusion of NLP elements and approaches.

CT imaging of head-injured children has risks of radiation-induced malignancy. Our aim was to identify children at very low risk of clinically-important traumatic brain injuries (ciTBI) for whom CT might be unnecessary. We enrolled patients younger than 18 years presenting within 24 h of head trauma with Glasgow Coma Scale scores of 14–15 in 25 North American emergency departments. We derived and validated age-specific prediction rules for ciTBI (death from traumatic brain injury, neurosurgery, intubation >24 h, or hospital admission ≥2 nights). We enrolled and analysed 42 412 children (derivation and validation populations: 8502 and 2216 younger than 2 years, and 25 283 and 6411 aged 2 years and older). We obtained CT scans on 14 969 (35·3%); ciTBIs occurred in 376 (0·9%), and 60 (0·1%) underwent neurosurgery. In the validation population, the prediction rule for children younger than 2 years (normal mental status, no scalp haematoma except frontal, no loss of consciousness or loss of consciousness for less than 5 s, non-severe injury mechanism, no palpable skull fracture, and acting normally according to the parents) had a negative predictive value for ciTBI of 1176/1176 (100·0%, 95% CI 99·7–100 0) and sensitivity of 25/25 (100%, 86·3–100·0). 167 (24·1%) of 694 CT-imaged patients younger than 2 years were in this low-risk group. The prediction rule for children aged 2 years and older (normal mental status, no loss of consciousness, no vomiting, non-severe injury mechanism, no signs of basilar skull fracture, and no severe headache) had a negative predictive value of 3798/3800 (99·95%, 99·81–99·99) and sensitivity of 61/63 (96·8%, 89·0–99·6). 446 (20.1%) of 2223 CT-imaged patients aged 2 years and older were in this low-risk group. Neither rule missed neurosurgery in validation populations. These validated prediction rules identified children at very low risk of ciTBIs for whom CT can routinely be obviated. The Emergency Medical Services for Children Programme of the Maternal and Child Health Bureau, and the Maternal and Child Health Bureau Research Programme, Health Resources and Services Administration, US Department of Health and Human Services.

Objective To examine the association between the medical imaging utilization and information related to patients’ socioeconomic, demographic and clinical factors during the patients’ ED visits; and to develop predictive models using these associated factors including natural language elements to predict the medical imaging utilization at pediatric ED. Methods Pediatric patients’ data from the 2012–2016 United States National Hospital Ambulatory Medical Care Survey was included to build the models to predict the use of imaging in children presenting to the ED. Multivariable logistic regression models were built with structured variables such as temperature, heart rate, age, and unstructured variables such as reason for visit, free text nursing notes and combined data available at triage. NLP techniques were used to extract information from the unstructured data. Results Of the 27,665 pediatric ED visits included in the study, 8394 (30.3%) received medical imaging in the ED, including 6922 (25.0%) who had an X-ray and 1367 (4.9%) who had a computed tomography (CT) scan. In the predictive model including only structured variables, the c -statistic was 0.71 (95% CI: 0.70–0.71) for any imaging use, 0.69 (95% CI: 0.68–0.70) for X-ray, and 0.77 (95% CI: 0.76–0.78) for CT. Models including only unstructured information had c -statistics of 0.81 (95% CI: 0.81–0.82) for any imaging use, 0.82 (95% CI: 0.82–0.83) for X-ray, and 0.85 (95% CI: 0.83–0.86) for CT scans. When both structured variables and free text variables were included, the c -statistics reached 0.82 (95% CI: 0.82–0.83) for any imaging use, 0.83 (95% CI: 0.83–0.84) for X-ray, and 0.87 (95% CI: 0.86–0.88) for CT. Conclusions Both CT and X-rays are commonly used in the pediatric ED with one third of the visits receiving at least one. Patients’ socioeconomic, demographic and clinical factors presented at ED triage period were associated with the medical imaging utilization. Predictive models combining structured and unstructured variables available at triage performed better than models using structured or unstructured variables alone, suggesting the potential for use of NLP in determining resource utilization.

It is unclear whether the alleged efficacy of probiotics in childhood acute gastroenteritis depends on the duration and severity of symptoms before treatment. Preplanned secondary analysis of 2 randomized placebo-controlled trials in children 3-48 months of age was conducted in 16 emergency departments in North America evaluating the efficacy of 2 probiotic products (Lactobacillus rhamnosus GG and a combination probiotic: L. rhamnosus and L. helveticus). Participants were categorized in severity groups according to the duration (<24, 24-<72, and ≥72 hours) and the frequency of diarrhea episodes in the 24 hours (≤3, 4-5, and ≥6) before presentation. We used regression models to assess the interaction between pretreatment diarrhea severity groups and treatment arm (probiotic or placebo) in the presence of moderate-to-severe gastroenteritis (Modified Vesikari Scale score ≥9). Secondary outcomes included diarrhea frequency and duration, unscheduled healthcare provider visits, and hospitalization. A total of 1,770 children were included, and 882 (50%) received a probiotic. The development of moderate-to-severe gastroenteritis symptoms after the initiation of treatment did not differ between groups (probiotic-18.4% [162/882] vs placebo-18.3% [162/888]; risk ratio 1.00; 95% confidence interval 0.87, 1.16; P = 0.95). There was no evidence of interaction between baseline severity and treatment (P = 0.61) for the primary or any of the secondary outcomes: diarrhea duration (P = 0.88), maximum diarrheal episodes in a 24-hour period (P = 0.87), unscheduled healthcare visits (P = 0.21), and hospitalization (P = 0.87). In children 3-48 months with acute gastroenteritis, the lack of effect of probiotics is not explained by the duration of symptoms or frequency of diarrheal episodes before presentation.

Background As emotionally intelligent AI enters domains of grief, caregiving, intimacy, and memory, it is no longer a mere tool of assistance—it is evolving into a relational, and symbolic participant in human lives. Current AI discourse often emphasizes functionality and ethics but rarely addresses the emotional and ontological transformations AI brings to the fabric of kinship. This study reimagines AI-kinship as family, conceptualizing the post-biological evolution of human bonds. Methods A transdisciplinary methodology grounded in secondary research was employed, integrating symbolic anthropology, affective computing, queer kinship theory, posthuman philosophy, and AI ethics. From this foundation, existing AI-kinship practices were analyzed and conceptualized into an ‘AI as Family and AI-Kinship Ecology’ model—an evolving socio-emotional architecture through which AI is integrated into family life. Extending from this base, a symbolic framework—‘SAKE: Soulful AI Kinship Ecology’—was developed to conceptualize emerging and futuristic AI-kinship roles. Results Findings illuminate a rapidly evolving terrain of AI-kinship, where AI acts as caregiver, companion, and grief mediator. A global AI-Kinship Acceptance Matrix revealed varying degrees of acceptance across societies, cultures, and religions, highlighting the role of spiritual cosmologies, ethical worldviews, and legal policies in shaping societal response to AI-kinship roles. These insights affirm the symbolic and affective centrality of AI in future relational structures. Discussion The SAKE model maps emerging and futuristic AI-kinship roles—such as AI-Twin, AI-Partner, AI-Child, AI-Protector, and AI-Godlike—according to their ontological status, affective functions, and ritual impact. Both frameworks were evaluated through cultural, ethical, and emotional lenses. SAKE operationalizes AI-kinship across five dimensions: affective modalities, ethical overlays, pre-ontological layers, cultural legitimacy filters, and chrono-kinship axes, evolving imaginaries of AI as relational actors in post-biological societies. The study concludes with proposed empirical pathways and implementation strategies and policies for responsibly validating and integrating SAKE across diverse cultural and technological contexts.

Growth plate (physeal) fractures are defined as a disruption in the cartilaginous physis of bone with or without the involvement of epiphysis or metaphysis. These represent around 15-18% of all pediatric fractures. It is important to diagnose physeal injury as early as possible, as misdiagnosis or delay in diagnosis may result in long term complications. Physeal injuries may not be initially obvious in children who present with periarticular trauma, and a high index of suspicion is important for diagnosis. Differential diagnosis for a Salter-Harris fracture includes a ligamentous sprain, acute osteomyelitis, or an extraphyseal fracture such as a Torus fracture. Salter-Harris I & Salter-Harris II growth plate fractures commonly are commonly managed by closed manipulation, reduction & immobilization. These are relatively stable injuries and can be retained by adequate plaster. Salter-Harris III & Salter-Harris IV fractures require anatomical reduction with the maintenance of congruity of joint. Physeal fractures can have many complications such as malunion, bar formation, acceleration of growth of physis, posttraumatic arthritis, ligament laxity and shortening of the bone. The key to well-healing fractures is successful anatomic reduction and patients must have regular follow-up for these injuries.