Aid Health Care Research Articles

A happiness approach to valuing health states for children

Preference weights are widely used to score generic health states into utility indexes for estimation of quality adjusted life years (QALYs) and to aid health care funding decisions. To date, health state utilities are predominantly derived using stated preference methods based on decision utility. This paper tests an alternative and generates preference weights using experienced utility for children based on the Child Health Utility 9D (CHU9D) descriptive system. We estimate the relative values of the CHU9D health states with regard to experienced utility, where experienced utility is approximated by self-reported happiness. A nationally-representative longitudinal survey was used including 6090 Australian children aged 12–17 years surveyed over 2014–2018. The derived weights were then applied to calculate the utility decrements for a few common child health conditions. We found that the estimated utility decrements are largely similar to those estimated using the published CHU9D Australian adolescent weights based on decision utility, except for pain and depression. A smaller utility decrement for pain and a larger utility decrement for depression were indicated by experienced utility. We contribute to the literature by showing that using experienced utility methods to generate preference weights for health states is possible, and we discuss some important methodological challenges for future studies such as the impracticability of anchoring to ‘dead’ when utilizing experienced utility.

Occlusal Radiographs in a Turkish Pediatric Subpopulation.

Dental radiographs are a useful diagnostic aid in the oral health care and dental treatment of children. The most commonly used radiographs are periapical and panoramic radiographs. Occlusal radiographs are preferred in more specific cases. The aim of this study was to retrospectively evaluate the reasons for obtaining occlusal radiographs in pediatric patients. Occlusal radiographs of patients aged 1-13 years who attended the Pediatric Dentistry Clinic of Ondokuz Mayıs University Faculty of Dentistry between 01 August 2015 and 01 August 2020 were retrospectively evaluated. Patients with syndromes and diseases that could cause cleft lip and palate and dental anomalies were not included in the study. The medical history of the 354 patients who were included in the study was provided by the automated system. A total of 359 occlusal radiographs from these patients were analysed. The number of male patients included in the study was 208 and the number of female patients was 146. Occlusal radiographs of the maxilla of 312 patients were taken, of the mandible of 37 patients, and both the upper and lower jaws of 5 patients. It was found that occlusal radiographs were taken mostly for dental trauma in the permanent dentition (156), injuries in the primary dentition (68) and for the diagnosis of dental anomalies (57). Occlusal radiographs are taken for specific situations in pediatric dentistry and are particularly useful in the detection and diagnosis of primary and permanent tooth injuries.

Estimation of Pro-Inflammatory Markers In Night Shift Healthcare Workers And Identify Risk Factors Associated With It

Shift labor, with hours outside of the regular diurnal work cycle, is used to facilitate round-the-clock emergency healthcare services. This impacts the circadian rhythm, which can potentially activate the pro-inflammatory responses of the immune system. This study aimed to find out the levels of pro-inflammatory markers in night shift healthcare workers and find risk factors associated with their increase. 303 participants (doctors, nurses, technicians, health care aids) were recruited with 169 participants nightshift and 134 dayshift workers. Pro-inflammatory markers Highly Sensitive C-Reactive Protein (hsCRP) and Total Leukocyte Count (TLC)] were estimated. Factors were compared by t test, Wilcoxon Rank Sum test and Chi square test. Risk factors for hsCRP elevation were identified by logistic regression analysis. Pro-inflammatory markers like hsCRP and TLC were significantly increased in night shift health workers in comparison to the day shift workers (hsCRP 0.30± 0.33 mg/dl vs 0.10± 0.09 mg/dl, TLC 8181.06± 1181.53 /cu mm vs 7473.80± 1018.81 /cu mm) respectively. Multivariate logistic regression analysis showed night shift work (OR 48.20 95% CI (4.99-465.61), age (OR 1.13 95% CI (1.01-1.26)) and BMI (OR 1.18 95% CI (1.06-1.32)) as independent predictors of elevated hsCRP after adjusting for other risk factors. Hence, night-shift work is an independent risk factor for a rise in pro-inflammatory markers. These findings might aid health care workers and policymakers strategize methods to tackle the challenges through providing support programs, counselling sessions to avoid ailments and deliver health services in a better way.

Practical Approach to Diagnosis and Management of IL-1-Mediated Autoinflammatory Diseases (CAPS, TRAPS, MKD, and DIRA).

Systemic autoinflammatory diseases (SAIDs) are a group of rare genetic and nongenetic immune dysregulatory disorders associated with high morbidity and mortality if left untreated. Therefore, early diagnosis and initiation of targeted treatment is vital in SAID patients to control the disease activity and prevent long-term immune-mediated damage. A specific group of genetically defined SAIDs is associated with increased inflammasome-mediated production of active interleukin (IL)-1. Even though progress in immunobiology and genetics has brought forth diagnostic tools and novel treatments that have been described in the literature extensively, many challenges remain in the clinical setting. Some challenges that health care providers may face on a day-to-day basis include the requirement of a multidisciplinary approach due to the complexity of these diseases, limited evidence-based treatment options, and barriers to access available therapies. Primarily, IL-1 inhibitors anakinra, canakinumab, and rilonacept are used to control the inflammation in these patients, with the goal of achieving sustainable remission. Recently published provisional points to consider from the European Alliance of Associations for Rheumatology (EULAR) and American College of Rheumatology (ACR) provide diagnosis, management, and monitoring recommendations for four IL-1-mediated autoinflammatory diseases: cryopyrin-associated periodic syndromes (CAPS), tumour necrosis factor receptor-associated periodic syndrome (TRAPS), mevalonate kinase deficiency (MKD), and deficiency of the IL-1 receptor antagonist (DIRA). The goal of this paper is to aid health care professionals by providing a practical approach to diagnosis and management of these four IL-1 mediated SAIDs on the basis of the recent EULAR/ACR recommendations.

Resident-, family-, and staff-identified goals for rehabilitation of long-term care residents with dementia: a qualitative study

BackgroundLong-term care (LTC) residents with dementia can benefit from rehabilitation to improve function and quality of life. However, specific goals for rehabilitation with this population are not always clear. The purpose of this study was to describe the goals for rehabilitation for LTC residents with dementia from the perspective of residents, family, and staff.MethodsThis was a phenomenological qualitative study. LTC residents with moderate to severe dementia, family members, and staff were recruited from two LTC homes in Halifax, Nova Scotia. Data were collected through semi-structured interviews and field notes from observations with residents while they were being active within the home. Data were analyzed via the principles of thematic content analysis, mapped onto the International Classification of Functioning, Disability, and Health (ICF) Model, and reported by the participant group (i.e., residents, family, or staff).ResultsThe 15 participants were three female residents aged 82 to 98 years, seven predominantly (86%) female family members aged 56 to 74 years, and five staff members (two females, three males, aged 22 to 55 years) who were physiotherapists, a physiotherapy assistant, a healthcare aide, and a registered licenced practical nurse. Most identified goals fell within the activities and participation constructs of the ICF model and focused on maintaining or improving function, mobility, and quality of life. Specific themes included preventing falls, walking or locomoting, stair climbing, maintaining activities of daily living, engaging in enjoyable exercise, maintaining independence and human connections, keeping busy, leaving the home for activities, and participating in group activities.ConclusionsRehabilitation goals for LTC residents living with dementia often focus on quality of life and functional activities and participation in LTC and family activities and events. Function and quality of life are interrelated, whereby functional goals influence quality of life. While some goals focus on improvement in function, maintenance or prevention of decline were also key elements. Future work should ensure rehabilitation interventions are developed relative to individually identified goals, and interventional success is measured in relation to the goal.

In situ MgO nanoparticle-doped Janus electrospun dressing against bacterial invasion and immune imbalance for irregular wound healing.

Owing to the unpredictable size of wounds and irregular edges formed by trauma, nanofibers' highly customizable and adherent in situ deposition can contribute to intervention in the healing process. However, electrospinning is limited by the constraints of conventional polymeric materials despite its potential for anti-inflammatory and antimicrobial properties. Here, inspired by the Janus structure and biochemistry of nanometal ions, we developed an in situ sprayed electrospinning method to overcome bacterial infections and immune imbalances during wound healing. The bilayer fiber scaffold has a hydrophobic outer layer composed of polycaprolactone (PCL) and a hydrophilic inner layer composed of gelatin, poly(L-lactic acid) (PLLA), and magnesium oxide nanoparticles, constituting the PCL/PLLA-gelatin-MgO (PPGM) electrospun scaffold. This electrospun scaffold blocked the colonization and growth of bacteria and remained stable on the wound for continuous anti-inflammatory properties to promote wound healing. Furthermore, PPGM electrospinning modulated collagen deposition and the inflammatory microenvironment in the full-thickness skin model, significantly accelerating vascularization and epithelialization progression. This personalized Janus electrospun scaffold has excellent potential as a new type of wound dressing for first aid and wound healthcare.

Comparison of Acute Health Care Utilization Between Patients Receiving In-Center Hemodialysis and the General Population: A Population-Based Matched Cohort Study From Ontario, Canada.

Patients receiving maintenance hemodialysis have multiple comorbidities and are at high risk of presenting to the hospital. However, the incidence and cost of acute health care utilization in the in-center hemodialysis population and how this compares with other populations is poorly understood. To determine the rate, pattern, and cost of emergency department visits and hospitalizations in patients receiving in-center hemodialysis compared with a matched general population. Population-based matched cohort study. We used linked administrative health care databases from Ontario, Canada. We included 25 379 patients (incident and prevalent) receiving in-center hemodialysis between January 1, 2010, and December 31, 2018. Patients were matched on birth date (±2 years), sex, and cohort entry date using a 1:4 ratio to 101 516 individuals from the general population. Our primary outcomes were emergency department visits (allowing for multiple visits per individual) and hospital admissions from the emergency department. We also assessed all-cause hospitalizations, all-cause readmissions within 30 days of discharge from the original hospitalization, length of stay for hospital admissions (including multiple visits per individual), and the financial cost of these admissions. We presented the rate, percentage, median (25th, 75th percentiles), and incidence rate per 1000 person-years for emergency department visits and hospitalizations. Individual-level health care costs for emergency department visits and all-cause hospitalization were estimated using resource intensity weights multiplied by the cost per weighted case. Patients receiving in-center hemodialysis had substantially more comorbidities (eg, diabetes) than the matched general population. Eighty percent (n = 20 309) of patients receiving in-center hemodialysis had at least 1 emergency department visit compared with 56% (n = 56 452) of individuals in the matched general population, over a median follow-up of 1.8 years (25th, 75th percentiles: 0.7, 3.6) and 5.2 (2.5, 8.4) years, respectively. The incidence rate of emergency department visits, allowing for multiple visits per individual, was 2274 per 1000 person-years (95% confidence interval [CI]: 2263, 2286) for patients receiving in-center hemodialysis, which was almost 5 times as high as the matched general population (471 per 1000 person-years; 95% CI: 469, 473). The rate of hospital admissions from the emergency department and the rate of all-cause hospital admissions in the in-center hemodialysis population was more than 7 times as high as the matched general population (hospital admissions from the emergency department: 786 vs 101 per 1000 person-years; all-cause hospital admissions: 1056 vs 139 per 1000 person-years). The median number of all-cause hospitalization days per patient year was 4.0 (0, 16.5) in the in-center hemodialysis population compared with 0 (0, 0.5) in the matched general population. The cost per patient-year for emergency department visits in the in-center hemodialysis population was approximately 5.5 times as high as the matched general population while the cost of hospitalizations in the in-center hemodialysis population was approximately 11 times as high as the matched general population (emergency department visits: CAN$ 1153 vs CAN$ 209; hospitalizations: CAN$ 21 151 vs CAN$ 1873 [all costs in 2023 CAN$]). External generalizability and we could not determine whether emergency department visits and hospitalizations were preventable. Patients receiving in-center hemodialysis have high acute health care utilization. These results improve our understanding of the burden of disease and the associated costs in the in-center hemodialysis population, highlight the need to improve acute outcomes, and can aid health care capacity planning. Additional research is needed to address the risk of hospitalization after controlling for patient comorbidities. This is not applicable as this is a population-based matched cohort study and not a clinical trial.

AN UNEQUAL WORK ENVIRONMENT: NURSING HOME STAFF PERCEPTIONS OF UNIT ORGANIZATIONAL CONTEXT

Abstract The organizational context in nursing homes is associated with quality of care and residents’ quality of life. Our objective was to examine nursing home staff perceptions of unit organizational context. We conducted a secondary analysis of cross-sectional data. Participants were 3765 unregulated (health care aides (HCAs)) and 1130 regulated staff (managers, registered nurses, registered psychiatric nurses (RN/RPNs) and licensed practical nurses (LPNs)) from 91 nursing homes in Western Canada. We measured nursing home staff’s perceptions of organizational context via the Alberta Context Tool (ACT), comprised of ten concepts: leadership, culture, evaluation, social capital, formal interactions, informal interactions, organizational slack (staff, space, time). Differences between groups were examined via one-way ANOVA. Managers reported significantly higher organizational context scores compared to all other staff members in care homes, followed by RN/RPNs. LPNs and HCAs reported the lowest scores. Specifically, LPNs reported the lowest scores on all organizational context measures except for structural resources and organizational slack space, for which HCAs reported the lowest scores. HCAs reported higher scores than LPNs on unit leadership, culture, evaluation, social capital. When compared with managers and RN/RPNs in the nursing home, LPNs reported the lowest SF-8 mental health scores (49.30 (10.05)) and HCAs reported the lowest physical health scores (48.53 (8.16)). Our work demonstrates the need for improvement of staffing and time for staff in nursing homes. Specific attention is needed to focus on modifiable organizational unit context factors for LPNs and HCAs, who are responsible for providing the majority of the direct care for residents.

Applying the Social Ecological Model to Understand Support for Home Healthcare Workers and Covid-19 Vaccine Uptake in N.J.

More than 3.6 M home healthcare aides are key parts of the U.S. healthcare workforce, serving older adults and individuals with disabilities living in the community. This study used a social ecological framework (SEM) to identify facilitators and barriers to Covid-19 vaccination among home healthcare aides and administrators in N.J. Key informant interviews with a purposive sample of home healthcare aides along with federal and state policy review were used to develop a survey of home healthcare administrators (HHA), which was sent to all home healthcare agencies in N.J. Results show that completed vaccination series rates among HHA were the same or higher than statewide and national rates, but there was skepticism about the utility of a booster among both aides and HHA. While organizational policy and information support around Covid-19 from the agencies to the staff was inconsistent, overall agencies acted as a driver for higher vaccination rates. At the intrapersonal level, individual self-assessment of risk was not consistent with actual risk, impeding vaccination. When all levels of the SEM are working in concert it can increase completed vaccinations. Key areas to target addressing the call for yearly boosters include organizational policy and intrapersonal factors aligning perceived and actual risk.

Research Priorities of the Pediatric Spinal Cord Injury Population: An International Insight for Rehabilitation Care

BackgroundAlthough feedback from people with adult-onset spinal cord injury (SCI) has been considered for new rehabilitation programs, little is known about the priorities of the pediatric-onset SCI population. This study describes and compares health and life (H&L) domain research priorities of youth with pediatric-onset SCI and their parents/caregivers. MethodsA cross-sectional survey, designed by the Pan-European Paediatric Spinal Cord Injury (PEPSCI) Collaboration, was performed at six European countries. Dyad data from 202 participants, youth with pediatric-onset SCI (n = 101) and their parents/caregivers (n = 101), were analyzed with the PEPSCI H&L domain surveys. ResultsThe cohort was composed of 8 to 12-year-olds (30.7%), 13 to 17-year-olds (38.6%), and 18 to 25-year-olds (30.7%). The top three H&L domain research priorities reported by parents/caregivers of 8 to 12-year-olds were “walking/ability to move” (91%), “bladder” function (90%), and “general health/feel” (89%), compared with “physical function” (93%), “general health/feel” (90%), and “walking/ability to move” (89%) rated by parents/caregivers of 13 to 25-year-olds. “Bowel” function (85%), “leg/foot movement” (84%), and “bladder” function (84%) were reported as priorities by 13 to 25-year-olds, whereas “physical function” (84%), “experience at school” (83%), and “general mood” were highlighted by 8 to 12-year-olds. The top 10 priorities preferred by 13 to 25-year-olds when compared with the top 10 priorities reported by their parents/caregivers, included problems related to “bowel” and “pain.” ConclusionsHealth domain research priorities were highlighted by 13 to 25-year-olds, compared with their parents/caregivers who equally identified H&L domains. This survey will aid health care and clinical research organizations to engage stakeholders to implement a comprehensive research strategy for the pediatric SCI population.

Brain Tumor Detection Using Deep Learning

Brain tumor detection is a critical task in neuroimaging that significantly impacts clinical diagnosis and treatment planning. Conventional methods for tumor detection rely on manual interpretation of medical imaging data, which can be time-consuming and subject to interobserver variability. With the advancements in deep learning, particularly in the realm of convolutional neural networks (CNNs), there has been a growing interest in leveraging these techniques for automated brain tumor detection. This study explores the efficacy of deep learning models in the automated detection and classification of brain tumors from magnetic resonance imaging (MRI) scans. A comprehensive dataset of MRI images representing various tumor types and anatomical regions is employed to train and evaluate the deep learning models. The developed CNN-based architectures are designed to capture intricate patterns and features within the MRI scans, facilitating accurate tumor localization and classification. The research investigates the performance of the deep learning models in distinguishing between healthy brain tissue and different tumor subtypes, including gliomas, meningiomas, and metastatic tumors. Performance metrics such as accuracy, sensitivity, specificity, and area under the curve (AUC) are employed to assess the models' ability to precisely identify tumor regions and provide clinically relevant information to aid healthcare professionals. Furthermore, the study addresses challenges associated with model generalization, interpretability, and scalability for deployment in clinical settings. Strategies for optimizing model robustness, enhancing interpretability, and ensuring seamless integration into existing diagnostic workflows are explored. The findings of this research contribute to the growing body of knowledge regarding the application of deep learning in medical imaging analysis, particularly in the domain of brain tumor detection. The implications of this study extend to the development of more efficient and accurate tools to assist radiologists and clinicians in diagnosing brain tumors, thereby potentially improving patient outcomes and treatment strategies.

Identification of Emotional Spectrums of Patients Taking an Erectile Dysfunction Medication: Ontology-Based Emotion Analysis of Patient Medication Reviews on Social Media.

Patient medication reviews on social networking sites provide valuable insights into the experiences and sentiments of individuals taking specific medications. Understanding the emotional spectrum expressed by patients can shed light on their overall satisfaction with medication treatment. This study aims to explore the emotions expressed by patients taking phosphodiesterase type 5 (PDE5) inhibitors and their impact on sentiment. This study aimed to (1) identify the distribution of 6 Parrot emotions in patient medication reviews across different patient characteristics and PDE5 inhibitors, (2) determine the relative impact of each emotion on the overall sentiment derived from the language expressed in each patient medication review while controlling for different patient characteristics and PDE5 inhibitors, and (3) assess the predictive power of the overall sentiment in explaining patient satisfaction with medication treatment. A data set of patient medication reviews for sildenafil, vardenafil, and tadalafil was collected from 3 popular social networking sites such as WebMD, Ask-a-Patient, and Drugs.com. The Parrot emotion model, which categorizes emotions into 6 primary classes (surprise, anger, love, joy, sadness, and fear), was used to analyze the emotional content of the reviews. Logistic regression and sentiment analysis techniques were used to examine the distribution of emotions across different patient characteristics and PDE5 inhibitors and to quantify their contribution to sentiment. The analysis included 3070 patient medication reviews. The most prevalent emotions expressed were joy and sadness, with joy being the most prevalent among positive emotions and sadness being the most prevalent among negative emotions. Emotion distributions varied across patient characteristics and PDE5 inhibitors. Regression analysis revealed that joy had the strongest positive impact on sentiment, while sadness had the most negative impact. The sentiment score derived from patient reviews significantly predicted patient satisfaction with medication treatment, explaining 19% of the variance (increase in R2) when controlling for patient characteristics and PDE5 inhibitors. This study provides valuable insights into the emotional experiences of patients taking PDE5 inhibitors. The findings highlight the importance of emotions in shaping patient sentiment and satisfaction with medication treatment. Understanding these emotional dynamics can aid health care providers in better addressing patient needs and improving overall patient care.

Cohort profile: recruitment and retention in a prospective cohort of Canadian healthcare workers during the COVID-19 pandemic

PurposeHealthcare workers were recruited early in 2020 to chart effects on their health as the COVID-19 pandemic evolved. The aim was to identify modifiable workplace risk factors for infection and...

AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study

Background Overcrowding of hospitals and emergency departments (EDs) is a growing problem. However, not all ED consultations are necessary. For example, 80% of patients in the ED with chest pain do not have an acute coronary syndrome (ACS). Artificial intelligence (AI) is useful in analyzing (medical) data, and might aid health care workers in prehospital clinical decision-making before patients are presented to the hospital. Objective The aim of this study was to develop an AI model which would be able to predict ACS before patients visit the ED. The model retrospectively analyzed prehospital data acquired by emergency medical services' nurse paramedics. Methods Patients presenting to the emergency medical services with symptoms suggestive of ACS between September 2018 and September 2020 were included. An AI model using a supervised text classification algorithm was developed to analyze data. Data were analyzed for all 7458 patients (mean 68, SD 15 years, 54% men). Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for control and intervention groups. At first, a machine learning (ML) algorithm (or model) was chosen; afterward, the features needed were selected and then the model was tested and improved using iterative evaluation and in a further step through hyperparameter tuning. Finally, a method was selected to explain the final AI model. Results The AI model had a specificity of 11% and a sensitivity of 99.5% whereas usual care had a specificity of 1% and a sensitivity of 99.5%. The PPV of the AI model was 15% and the NPV was 99%. The PPV of usual care was 13% and the NPV was 94%. Conclusions The AI model was able to predict ACS based on retrospective data from the prehospital setting. It led to an increase in specificity (from 1% to 11%) and NPV (from 94% to 99%) when compared to usual care, with a similar sensitivity. Due to the retrospective nature of this study and the singular focus on ACS it should be seen as a proof-of-concept. Other (possibly life-threatening) diagnoses were not analyzed. Future prospective validation is necessary before implementation.

Expanding horizons and navigating challenges for enhanced clinical workflows: ChatGPT in urology.

ChatGPT has emerged as a potential tool for facilitating doctors' workflows. However, when it comes to applying these findings within a urological context, there have not been many studies. Thus, our objective was rooted in analyzing the pros and cons of ChatGPT use and how it can be exploited and used by urologists. ChatGPT can facilitate clinical documentation and note-taking, patient communication and support, medical education, and research. In urology, it was proven that ChatGPT has the potential as a virtual healthcare aide for benign prostatic hyperplasia, an educational and prevention tool on prostate cancer, educational support for urological residents, and as an assistant in writing urological papers and academic work. However, several concerns about its exploitation are presented, such as lack of web crawling, risk of accidental plagiarism, and concerns about patients-data privacy. The existing limitations mediate the need for further improvement of ChatGPT, such as ensuring the privacy of patient data and expanding the learning dataset to include medical databases, and developing guidance on its appropriate use. Urologists can also help by conducting studies to determine the effectiveness of ChatGPT in urology in clinical scenarios and nosologies other than those previously listed.

An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study.

Within the trauma system, the emergency department (ED) is the hospital's first contact and is vital for allocating medical resources. However, there is generally limited information about patients that die in the ED. The aim of this study was to develop an artificial intelligence (AI) model to predict trauma mortality and analyze pertinent mortality factors for all patients visiting the ED. We used the Korean National Emergency Department Information System (NEDIS) data set (N=6,536,306), incorporating over 400 hospitals between 2016 and 2019. We included the International Classification of Disease 10th Revision (ICD-10) codes and chose the following input features to predict ED patient mortality: age, sex, intentionality, injury, emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and vital signs. We compared three different feature set performances for AI input: all features (n=921), ICD-10 features (n=878), and features excluding ICD-10 codes (n=43). We devised various machine learning models with an ensemble approach via 5-fold cross-validation and compared the performance of each model with that of traditional prediction models. Lastly, we investigated explainable AI feature effects and deployed our final AI model on a public website, providing access to our mortality prediction results among patients visiting the ED. Our proposed AI model with the all-feature set achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.9974 (adaptive boosting [AdaBoost], AdaBoost + light gradient boosting machine [LightGBM]: Ensemble), outperforming other state-of-the-art machine learning and traditional prediction models, including extreme gradient boosting (AUROC=0.9972), LightGBM (AUROC=0.9973), ICD-based injury severity scores (AUC=0.9328 for the inclusive model and AUROC=0.9567 for the exclusive model), and KTAS (AUROC=0.9405). In addition, our proposed AI model outperformed a cutting-edge AI model designed for in-hospital mortality prediction (AUROC=0.7675) for all ED visitors. From the AI model, we also discovered that age and unresponsiveness (coma) were the top two mortality predictors among patients visiting the ED, followed by oxygen saturation, multiple rib fractures (ICD-10 code S224), painful response (stupor, semicoma), and lumbar vertebra fracture (ICD-10 code S320). Our proposed AI model exhibits remarkable accuracy in predicting ED mortality. Including the necessity for external validation, a large nationwide data set would provide a more accurate model and minimize overfitting. We anticipate that our AI-based risk calculator tool will substantially aid health care providers, particularly regarding triage and early diagnosis for trauma patients.

Usability and feasibility of PreventS-MD web app for stroke prevention.

Most strokes and cardiovascular diseases (CVDs) are potentially preventable if their risk factors are identified and well controlled. Digital platforms, such as the PreventS-MD web app (PreventS-MD) may aid health care professionals (HCPs) in assessing and managing risk factors and promoting lifestyle changes for their patients. This is a mixed-methods cross-sectional two-phase survey using a largely positivist (quantitative and qualitative) framework. During Phase 1, a prototype of PreventS-MD was tested internationally by 59 of 69 consenting HCPs of different backgrounds, age, sex, working experience, and specialties using hypothetical data. Collected comments/suggestions from the study HCPs in Phase 1 were reviewed and implemented. In Phase 2, a near-final version of PreventS-MD was developed and tested by 58 of 72 consenting HCPs using both hypothetical and real patient (n = 10) data. Qualitative semi-structured interviews with real patients (n = 10) were conducted, and 1 month adherence to the preventive recommendations was assessed by self-reporting. The four System Usability Scale (SUS) groups of scores (0-50 unacceptable; 51-68 poor; 68-80.3 good; >80.3 excellent) were used to determine usability of PreventS-MD. Ninety-nine HCPs from 27 countries (45% from low- to middle-income countries) participated in the study, and out of them, 10 HCPs were involved in the development of PreventS before the study, and therefore were not involved in the survey. Of the remaining 89 HCPs, 69 consented to the first phase of the survey, and 59 of them completed the first phase of the survey (response rate 86%), and 58 completed the second phase of the survey (response rate 84%). The SUS scores supported good usability of the prototype (mean score = 80.2; 95% CI [77.0-84.0]) and excellent usability of the final version of PreventS-MD (mean score = 81.7; 95% CI [79.1-84.3]) in the field. Scores were not affected by the age, sex, working experience, or specialty of the HCPs. One-month follow-up of the patients confirmed the high level of satisfaction/acceptability of PreventS-MD and (100%) adherence to the recommendations. The PreventS-MD web app has a high level of usability, feasibility, and satisfaction by HCPs and individuals at risk of stroke/CVD. Individuals at risk of stroke/CVD demonstrated a high level of confidence and motivation in following and adhering to preventive recommendations generated by PreventS-MD.

Swami Vivekananda's Enduring Legacy: Inspiring Education, Global Unity, and Social Transformation

Swami Vivekananda, a prominent Indian Hindu monk and philosopher, made significant contributions to education, spiritual understanding, and the global promotion of Indian philosophies. His emphasis on the essential unity of all religions and cultures promotes tolerance, respect, and a global perspective. His call for active learning, critical thinking, and the application of practical knowledge aligns with modern educational needs. His vision of education as a tool for social transformation and the importance of individuality and freedom in the learning process resonate with contemporary discussions on holistic development. The Ramakrishna Mission, established based on his principles, continues to impact society by providing education, healthcare, and humanitarian aid. Swami Vivekananda's teachings, rooted in spirituality and compassion, offer a timeless guide for individuals seeking personal growth and striving to make a positive difference in the world, making his philosophy a source of inspiration and guidance for humanity at large. His legacy continues to inspire people from diverse backgrounds to explore the depths of Vedanta, engage in social service, and contribute positively to a harmonious world.

A Bibliometric Analysis of the Top 100 Most Influential Studies on Robotic Arthroplasty.

The use of robotics in arthroplasty surgery has increased substantially in recent years. The purpose of this study was to objectively identify the 100 most influential studies in the robotic arthroplasty literature and to conduct a bibliometric analysis of these studies to describe their key characteristics. The Clarivate Analytics Web of Knowledge database was used to gather data and metrics for robotic arthroplasty research using Boolean queries. The search list was sorted in descending order by the number of citations, and articles were included or excluded based on clinical relevance to robotic arthroplasty. The top 100 studies were cited a total of 5770 times from 1997 to 2021, with rapid growth in both citation generation and the number of articles published occurring in the past 5years. The top 100 robotic arthroplasty articles originated from 12 countries, with the United States being responsible for almost half of the top 100. The most common study types were comparative studies (36) followed by case series (20), and the most common levels of evidence were III (23) and IV (33). Research on robotic arthroplasty is rapidly growing and originates from a wide variety of countries, academic institutions, and with significant industry influence. This article serves as a reference to direct orthopaedic practitioners to the 100 most influential studies in robotic arthroplasty. We hope that these 100 studies and the analysis we provide aid healthcare professionals in efficiently assessing consensus, trends, and needs within the field.

Spatiotemporal Data-Driven Multiperiod Relocation Optimization of Emergency Medical Services: Maximum Equality Objective

As a kind of first aid healthcare service, emergency medical services (EMSs) present high spatiotemporal sensitivity due to significant changes in the time-dependent urban environment. Taking full advantage of big spatiotemporal data to realize multiperiod relocation optimization of EMSs can reduce idle resources and improve service utilization efficiency and fairness. First, we established the dynamic time-dependent accessibility and equality model to formulate the multiperiod maximization objective of global equality. Second, we proposed a capacitated integer evolution algorithm that relocates emergency medical vehicles to optimize the scheduling scheme. Based on multiperiod mobile phone records and multiperiod online route planner data, the equality of EMSs in the research metropolis, Nanjing, China, rose by 41.5% on average, which has an incentivizing effect on alleviating the tension of prehospital service and minimizes accessibility disparities without constructing more infrastructure. We also created maps to visualize the changes in equality patterns over time. This relocation optimization approach can be regarded as a trade-off approach to dispatch time-dependent sensitive services and provide a practical tool for healthcare decision-makers to evaluate public healthcare systems and improve strategic urban service planning.