Single-Molecule Imaging of Telomerase RNA Reveals a Recruitment-Retention Model for Telomere Elongation.

Extension of telomeres is a critical step in the immortalization of cancer cells. This complex reaction requires proper spatiotemporal coordination of telomerase and telomeres and remains poorly understood at the cellular level. To understand how cancer cells execute this process, we combine CRISPR genome editing and MS2 RNA tagging to image single molecules of telomerase RNA (hTR). Real-time dynamics and photoactivation experiments of hTR in Cajal bodies (CBs) reveal that hTERT controls the exit of hTR from CBs. Single-molecule tracking of hTR at telomeres shows that TPP1-mediated recruitment results in short telomere-telomerase scanning interactions, and then base pairing between hTR and telomere ssDNA promotes long interactions required for stable telomerase retention. Interestingly, POT1 OB-fold mutations that result in abnormally long telomeres in cancers act by enhancing this retention step. In summary, single-molecule imaging unveils the life cycle of telomerase RNA and provides a framework to reveal how cancer-associated mutations mechanistically drive defects in telomere homeostasis.

2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation.

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017. Seven years after the last consensus, an updated document was considered necessary to define a contemporary framework for selection and management of patients considered for or undergoing catheter or surgical AF ablation. This consensus is a joint effort from collaborating cardiac electrophysiology societies, namely the European Heart Rhythm Association, the Heart Rhythm Society, the Asia Pacific Heart Rhythm Society, and the Latin American Heart Rhythm Society .

Are we assessing motor competence? Evidence-informed constructs for motor competence in preschoolers through an Exploratory Graph Analysis.

Motor competence (MC) is conceptually defined as a multidimensional latent construct that covers the proficient performance in motor skills and its underlying mechanisms This study aimed to statistically provide arguments that MC is a network of interconnected constructs, such as FMS, coordination, and its underlying mechanisms, which are responsible for preschoolers' proficiency in motor tasks. Participated 102 preschoolers (65 girls, M age = 4.22 ± 0.19) who were assessed for the Test of Gross Motor Development - 2nd edition, the Motor Competence Assessment, and the Supine-to-Stand. Data were explored using Exploratory Graph Analysis, using the EGAnet package in RStudio. A four-dimensional structure (61.2% of interactions) comprising tasks of the different protocols was underlined, in which all the nodes presented stable and adequate indexes (≥0.65; TEFI = -2.67). Four dimensions of MC were highlighted, namely Dimension 1, which combined movements for locomotor patterns; Dimension 2, comprising three process-oriented measures of object control skills to project objects; Dimension 3, which comprised of skills which require body coordination to displace body through space; and Dimension 4, composed by object control skills evaluated through product-oriented measures. For a better understanding of MC, the assessment of these different aspects that comprises MC should be considered.

Hidradenitis Suppurativa: Molecular Etiology, Pathophysiology, and Management-A Systematic Review.

Hidradenitis suppurativa is a chronic inflammatory skin condition that affects the hair follicles in areas of the body with apocrine glands. The condition is characterized by recurrent, painful nodules, abscesses, and draining sinuses that can lead to scarring and disfigurement. In this present study, we provide a focused evaluation of recent developments in hidradenitis suppurativa research, including novel therapeutics and promising biomarkers that may facilitate clinical diagnosis and treatment. We conducted a systematic review of controlled trials, randomized controlled trials, meta-analyses, case reports, and Cochrane Review articles in accordance with the PRISMA guidelines. The Cochrane Library, PubMed, EMBASE, and Epistemonikos databases were queried via Title/Abstract screen. Eligibility criteria included the following: (1) has a primary focus on hidradenitis suppurativa, (2) includes measurable outcomes data with robust comparators, (3) details the sample population, (4) English language, and (5) archived as full-text journal articles. A total of 42 eligible articles were selected for review. Qualitative evaluation identified numerous developments in our understanding of the disease's multiple potential etiologies, pathophysiology, and treatment options. It is important for individuals with hidradenitis suppurativa to work closely with a healthcare provider to develop a comprehensive treatment plan that addresses their individual needs and goals. To meet this objective, providers must keep current with developments in the genetic, immunological, microbiological, and environmental factors contributing to the disease's development and progression.

Determining Stress in Metallic Conducting Layers of Microelectronics Devices Using High Resolution Electron Backscatter Diffraction and Finite Element Analysis.

Delayed failure due to stress voiding is a concern with some aging microelectronics, as these voids can grow large enough to cause an open circuit. Local measurements of stress in the metallic layers are crucial to understanding and predicting this failure, but such measurements are complicated by the fact that exposing the aluminum conducting lines will relieve most of their stress. In this study, we instead mechanically thin the device substrate and measure distortions on the thinned surface using high resolution electron backscatter diffraction (HREBSD). These measurements are then related to the stresses in the metallic layers through elastic simulations. This study found that in legacy components that had no obvious voids, the stresses were comparable to the theoretical stresses at the time of manufacture (≈300 MPa). Distortion fields in the substrate were also determined around known voids, which may be directly compared to stress voiding models. The technique presented here for stress determination, HREBSD coupled with finite element analysis to infer subsurface stresses, is a valuable tool for assessing failure in layered microelectronics devices.

Characterization of Anomalous Grains in FeCo Magnetic Alloys.

Heat-treated FeCo-based magnetic alloys were characterized using a suite of electron microscopy techniques to gain insight into their structural properties. Electron channeling contrast imaging (ECCI) in the scanning electron microscope (SEM) found unique grains towards the outer edge of a FeCo sample with nonuniform background contrast. High-magnification ECCI imaging of these nonuniform grains revealed a weblike network of defects that were not observed in standard uniform background contrast grains. High-resolution electron backscattered diffraction (HR-EBSD) confirmed these defect structures to be dislocation networks and additionally found subgrain boundaries within the nonuniform contrast grains. The defect content within these grains suggests that they are unrecrystallized grains, and ECCI can be used as a rapid method to quantify unrecrystallized grains. To demonstrate the insight that can be garnered via ECCI on these unique grains, the sample was imaged before and after micro indentation. This experiment showed that slip bands propagate throughout the material until interacting with the dislocation networks, suggesting that these specific defects provide a barrier to plastic deformation. Taken together, these results show how ECCI can be used to better understand failure mechanisms in alloys and provides further evidence that dislocation networks play a critical role in the brittle failure of FeCo alloys.

Joy at work and vocational identity during COVID-19: A structural equation model.

OBJECTIVE: This study aims to explore the interrelations between joy at work, vocational identity, and COVID-19-related latent variables. DESIGN: A cross-sectional design was adopted to survey 253 registered nurses through nursing organizations and social networking sites during the time of the COVID-19 pandemic. METHODS: Structural Equation Modeling through Partial Least Squares (SEM-PLS) was employed to accurately analyze and estimate relationships among joy at work, vocational identity, and COVID-19-related constructs in compliance with published standards and guidelines. RESULTS: Thirty-one items received convergent validity measures to represent the five study constructs related to joy at work, vocational identity, and Work life Impact of COVID-19 pandemic: Meaning, Engagement, and Growth (MEG), Compassion, Sensitivity, and Respect (CSR), Trust, Support, and Flexibility (TSF), Vocational Identity Questionnaire (VIQ), and Work life Impact of COVID-19 pandemic (CI). The generated model exhibited good model fit and consistent quality indices. The data fit statistically with the model: (Average Path Coefficient = 0.195, p < 0.000); Average R-Squared = 0.156, p < 0.003; Average Adjusted R-Squared = 0.151, p < 0.004; Average Variance Inflation Factor = 2.193 (within the ideal range); Average Full collinearity VIF = 2.388 (within the ideal range), and Tenenhaus Goodness of Fit (GoF) = 0.329. The perceived Work life Impact of COVID-19 pandemic (CI) was a significant predictor of joy at work constructs (MEG, CSR, and TSF) and VIQ with direct effects. It also exerted a negative influence over MEG and TSF, but positively on CSR and VIQ. Notably, joy at work constructs significantly mediated the effects of CI on VIQ. CONCLUSION: A structural model was developed that offers insights and a parsimonious explanation for the interrelations of COVID-19, joy at work, and vocational identity variables. The pandemic unquestionably influences the nurses' perceived joy, sense of calling, and meaning in life's work, and joy at work enhances nurses' work-life worth during challenging times. CLINICAL RELEVANCE: An increased understanding of nurses' joy at work during challenging times can potentially influence work culture, informs individual nurses, nurse leaders, and healthcare systems in all professional domains of practice and provides the foundational information to identify barriers to joy and strategies that enhance joy.

Social connectedness and health-related quality of life among patients with cancer undergoing chemotherapy: A mixed method approach using structural equation modelling and photo-elicitation.

OBJECTIVE: To develop and test the model of association of social connectedness and health-related quality of life (HRQoL) among patients with cancer undergoing chemotherapy and to describe their social connectedness through photo-elicitation. BACKGROUND: Social connectedness has been empirically associated with several well-being outcomes. However, little is known on the association of social connectedness among patients with cancer undergoing chemotherapy. METHODS: Employing a mixed-methods design following the good reporting of a mixed methods study guideline, the quantitative approach involved 230 consecutively-selected patients with cancer undergoing chemotherapy who answered a three-part survey. From these patients, six informants participated in the photo-elicitation and key informant interview sessions. Gathered data were quantitatively analyzed using structural equation modelling and qualitatively processed using polytextual thematic analysis. RESULTS AND FINDINGS: Social connectedness had a positive influence on social well-being (ß = .22, p = .008) and emotional well-being (ß = .20, p = .023); however, it had a negative effect on functional well-being (ß = -.20, p = .007). The overall model revealed good model indices: χ2 /df = .82, RMSEA = .01, GFI = 1.00. Qualitative analysis culled from photo-elicitation uncovered five interconnected themes yielding the Honeycomb model of social connectedness which was composed of correspondence, cohesion, constitution, convergence and corroboration. CONCLUSION: HRQoL among patients with cancer undergoing chemotherapy is a multi-factorial health construct affected by social connectedness. The presented model emphasises the importance of social connectedness and paves way for the development of appropriate strategies to promote social connectedness among patients with cancer undergoing chemotherapy. RELEVANCE TO CLINICAL PRACTICE: The findings provide impetus in the modification and development of appropriate practices, policies and strategies in promoting social connectedness. These approaches are geared towards patient-family empowerment and health education techniques to ascertain that assistance from significant others is provided without limiting the patient's autonomy or independence.

Predictors of inpatient admission likelihood and prolonged length of stay among cerebrovascular disease patients: A nationwide emergency department sample analysis.

PURPOSE: To examine the hospital- and patient-related factors associated with increased likelihood of inpatient admission and extended hospitalization. METHODS: We applied multivariate logistic regression to a subset of ED hospital and patient characteristics linearly extrapolated from the 2019 National Emergency Department Sample database (n=626,508). Patient characteristics with 10 or fewer ED visits after national extrapolation were not reported in the current study to maintain patient confidentiality, in accordance with the HCUP Data Use Agreement. All selected ED visits represented a primary diagnosis of CVD (ICD-10 codes 160-168). All reported hospital and patient characteristics were subject to adjustment for covariates. P-values < 0.05 were considered statistically significant. MAIN FINDINGS: Medicare beneficiaries report higher inpatient admission rates than uninsured OR 0.81 (0.73-0.91) and privately insured OR 0.86 (0.79-0.94) individuals. Black and Native-American patients were 37% and 55% more likely to be hospitalized long (>75th percentile) (OR 1.37 [1.25-1.50], OR 1.55 [1.14-2.10]). Northeast emergency departments reported an increased odds of admission compared to the Midwest OR (0.40-0.62), South OR 0.79 (0.63-0.98) and West OR 0.52 (0.39-0.69). Patients with multiple comorbidities (mCCI = 3+) were 226% more likely to have a longer stay OR 3.26 (3.09-3.45) than patients presenting with zero or few comorbidities. Level I, II, and III trauma centers report distinctly high odds of inpatient admission (OR 3.54 [2.84-4.42], OR 2.68 [2.14-3.35], OR 1.51 [1.25-1.84]). PRINCIPAL CONCLUSIONS: Likelihoods of inpatient admission and long hospital stays were observably stratified through multiple, independently acting hospital and patient characteristics. Significant associations were stratified by race/ethnicity, location, and clinical presentation, among others. Attention to the factors reported here may serve well to mitigate emergency department crowding and its sobering impact on United States healthcare systems and patients.

The utility of machine learning algorithms for the prediction of patient-reported outcome measures following primary hip and knee total joint arthroplasty.

BACKGROUND: Patient-reported outcome measures (PROMs) are increasingly used as quality benchmark in total hip and knee arthroplasty (THA; TKA) due to bundled payment systems that aim to provide a patient-centered, value-based treatment approach. However, there is a paucity of predictive tools for postoperative PROMs. Therefore, this study aimed to develop and validate machine learning models for the prediction of numerous patient-reported outcome measures following primary hip and knee total joint arthroplasty. METHODS: A total of 4526 consecutive patients (2137 THA; 2389 TKA) who underwent primary hip and knee total joint arthroplasty and completed both pre- and postoperative PROM scores was evaluated in this study. The following PROM scores were included for analysis: HOOS-PS, KOOS-PS, Physical Function SF10A, PROMIS SF Physical and PROMIS SF Mental. Patient charts were manually reviewed to identify patient demographics and surgical variables associated with postoperative PROM scores. Four machine learning algorithms were developed to predict postoperative PROMs following hip and knee total joint arthroplasty. Model assessment was performed through discrimination, calibration and decision curve analysis. RESULTS: The factors most significantly associated with the prediction of postoperative PROMs include preoperative PROM scores, Charlson Comorbidity Index, American Society of Anaesthesiology score, insurance status, age, length of hospital stay, body mass index and ethnicity. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration and decision curve analysis. CONCLUSION: This study developed machine learning models for the prediction of patient-reported outcome measures at 1-year following primary hip and knee total joint arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four machine learning models, highlighting the potential of these models in clinical practice to inform patients prior to surgery regarding their expectations of postoperative functional outcomes following primary hip and knee total joint arthroplasty. LEVEL OF EVIDENCE: Level III, case control retrospective analysis.

Differences in incident and recurrent myocardial infarction among White and Black individuals aged 35 to 84: Findings from the ARIC community surveillance study.

BACKGROUND: No previous study has examined racial differences in recurrent acute myocardial infarction (AMI) in a community population. We aimed to examine racial differences in recurrent AMI risk, along with first AMI risk in a community population. METHODS: The community surveillance of the Atherosclerosis Risk in Communities Study (2005-2014) included 470,000 people 35 to 84 years old in 4 U.S. communities. Hospitalizations for recurrent and first AMI were identified from ICD-9-CM discharge codes. Poisson regression models were used to compare recurrent and first AMI risk ratios between Black and White residents. RESULTS: Recurrent and first AMI risk per 1,000 persons were 8.8 (95% CI, 8.3-9.2) and 20.7 (95% CI, 20.0-21.4) in Black men, 6.8 (95% CI, 6.5-7.0) and 14.1 (95% CI, 13.8-14.5) in White men, 5.3 (95% CI, 5.0-5.7) and 16.2 (95% CI, 15.6-16.8) in Black women, and 3.1 (95% CI, 3.0-3.3) and 8.8 (95% CI, 8.6-9.0) in White women, respectively. The age-adjusted risk ratios (RR) of recurrent AMI were higher in Black men vs White men (RR, 1.58 95% CI, 1.30-1.92) and Black women vs White women (RR, 2.09 95% CI, 1.64-2.66). The corresponding RRs were slightly lower for first AMI: Black men vs White men, RR, 1.49 (95% CI, 1.30-1.71) and Black women vs White women, RR, 1.65 (95% CI, 1.42-1.92) CONCLUSIONS: Large disparities exist by race for recurrent AMI risk in the community. The magnitude of disparities is stronger for recurrent events than for first events, and particularly among women.

Lattice QCD Calculation of the Pion Mass Splitting.

We use the infinite volume reconstruction method to calculate the charged and neutral pion mass difference. The hadronic tensor is calculated using lattice QCD and then combined with an analytic photon propagator, and the mass splitting is calculated with exponentially suppressed finite-volume errors. The calculation is performed using six gauge ensembles generated with 2+1-flavor domain wall fermions, and five ensembles are at the physical pion mass. Both Feynman and Coulomb gauges are adopted in the calculation and agree well when the lattice spacing approaches zero. After performing the continuum extrapolation and examining the residual finite-volume effects, we obtain the pion mass splitting Δm_{π}=4.534(42)_{stat}(43)_{sys} MeV, which agrees well with experimental measurements.

Prepubertal onset of type 2 diabetes in Shashi-Pena syndrome due to ASXL2 mutation.

Type 2 diabetes remains rare in the pediatric population and the majority of cases occur during puberty. A combination of genetic and environmental factors leads to the development of insulin resistance and ß-cell failure. An increased prevalence is recognized in a number of rare genetic disorders such as Alström and Bardet-Biedl syndromes. Recently, a rare neurodevelopmental disorder, Shashi-Pena syndrome due to the dominant negative effect of heterozygous mutations in additional Sex-Combs-Like Genes 2 (ASXL2) has been reported. ASXL2 null mice exhibit glucose intolerance, insulin resistance and lipodystrophy. The regulatory role of ASXL2 in glucose and lipid homeostasis occurs through its interaction with peroxisome proliferator-activated receptor gamma (PPARγ), a gene implicated in the pathogenesis of type 2 diabetes on genome-wide association studies. Thiazolidinediones, used for the treatment of type 2 diabetes, exert their effects as direct agonists of PPARγ. We report the first case of type 2 diabetes in Shashi-Pena syndrome, occurring in an 8-year-old prepubertal boy with no family history. In addition, the proband had dyslipidemia, and fatty infiltration of the liver with elevated transaminases. Mutation of ASXL2 in humans, through its interaction with PPARγ appears to cause a phenotype of insulin resistance, type 2 diabetes, and dyslipidemia. Further reported cases will assist in confirming this association.

Mechanism of FIB-Induced Phase Transformation in Austenitic Steel.

The transformation of unstable austenite to ferrite or α' martensite as a result of exposure to Xe+ or Ga+ ions at room temperature was studied in a 304 stainless steel casting alloy. Controlled Xe+ and Ga+ ion beam exposures of the 304 were carried out at a variety of beam/sample geometries. It was found that both Ga+ and Xe+ ion irradiation resulted in the transformation of the austenite to either ferrite or α' martensite. In this paper, we will refer to the transformation product as a BCC phase. The crystallographic orientation of the transformed area was controlled by the orientation of the austenite grain and was consistent with either the Nishiyama­Wasserman or the Kurdjumov­Sachs orientation relationships. On the basis of the Xe+ and Ga+ ion beam exposures, the transformation is not controlled by the chemical stabilization of the BCC phase by the ion species, but is a result of the disorder caused by the ion-induced recoil motion and subsequent return of the disordered region to a more energetically favorable phase.

Focused Ion Beam Preparation of Low Melting Point Metals: Lessons Learned From Indium.

Indium (In) and other low melting point metals are used as interconnects in a variety of hybridized circuits and a full understanding of the metallurgy of these interconnects is important to the reliability and performance of the devices. This paper shows that room temperature focused ion beam (FIB) preparation of cross-sections, using Ga+ or Xe+ can result in artifacts that obscure the true In microbump structure. The use of modified milling strategies to minimize the increased local sample temperature are shown to produce cross-sections that are representative of the In bump microstructure in some sample configurations. Furthermore, cooling of the sample to cryogenic temperatures is shown to reliably eliminate artifacts in FIB prepared cross-sections of In bumps allowing the true bump microstructure to be observed.

Immediate Interventions After Surgery to Reduce Readmission for Upper Extremity Infections.

ABSTRACT: Upper extremity infections (UEIs), commonly caused by intravenous drug use and direct trauma to the hand, can be devastating. Many studies support hand therapy postoperatively to improve outcomes. This population of patients often experience increased returns to the emergency department (ED) for treatment, repeat surgery, poor function, and increased hospital expenditure.This is a 2-phase study to investigate and intervene on the care pathway for patients undergoing surgery for UEI at a single urban university hospital. For the first 6-month phase, all patients with UEI requiring surgery received the standard of care, including written discharge instructions, a scheduled follow-up, and a determination for hand therapy based on evaluation. During the second phase, over the following 6 months, all patients with UEI requiring surgery received an intervention before discharge, in addition to standard of care, including a scripted educational session on the importance of hand therapy by a resident physician, home exercise program instructions, and a referral to hand therapy before discharge. Outcome measures were assessed for 6 months. Outcome measures included follow-up, hand therapy attendance, ED visits for the same complaint, readmissions, and reoperations.Forty-seven patients were in the first control group without intervention and 41 patients were in the second intervention group. Of the control group, 43% attended a follow-up appointment, 2% attended hand therapy, and 45% presented to the ED, resulting in a total of 167 visits. Eleven presented to the ED and were readmitted for complications from surgery, all 11 required a second operation. In the intervention group, 54% attended a follow-up appointment (P = 0.044), more patients attended hand therapy (22% P = 0.013), and 24% presented to the ED (P = 0.047), resulting in 40 ED visits.Seven were readmitted for complications from surgery and 4 required a second operation (P = 0.1). Early, simple intervention and education prove to be an effective method to decrease emergency department burden and healthcare expenditure for treatment of a challenging population. Although additional education and referral to hand therapy utilization do not change operation rates, it decreased returns to the ED, readmissions, and increased follow-up.

Neural network models accurately predict discharge disposition after revision total knee arthroplasty?

PURPOSE: Based on the rising incidence of revision total knee arthroplasty (TKA), bundled payment models may be applied to revision TKA in the near future. Facility discharge represents a significant cost factor for those bundled payment models; however, accurately predicting discharge disposition remains a clinical challenge. The purpose of this study was to develop and validate artificial intelligence algorithms to predict discharge disposition following revision total knee arthroplasty. METHODS: A retrospective review of electronic patient records was conducted to identify patients who underwent revision total knee arthroplasty. Discharge disposition was defined as either home discharge or non-home discharge, which included rehabilitation and skilled nursing facilities. Four artificial intelligence algorithms were developed to predict this outcome and were assessed by discrimination, calibration and decision curve analysis. RESULTS: A total of 2228 patients underwent revision TKA, of which 1405 patients (63.1%) were discharged home, whereas 823 patients (36.9%) were discharged to a non-home facility. The strongest predictors for non-home discharge following revision TKA were American Society of Anesthesiologist (ASA) score, Medicare insurance type and revision surgery for peri-prosthetic joint infection, non-white ethnicity and social status (living alone). The best performing artificial intelligence algorithm was the neural network model which achieved excellent performance across discrimination (AUC = 0.87), calibration and decision curve analysis. CONCLUSION: This study developed four artificial intelligence algorithms for the prediction of non-home discharge disposition for patients following revision total knee arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four candidate algorithms. Therefore, these models have the potential to guide preoperative patient counselling and improve the value (clinical and functional outcomes divided by costs) of revision total knee arthroplasty patients. LEVEL OF EVIDENCE: IV.

Machine learning algorithms predict extended postoperative opioid use in primary total knee arthroplasty.

PURPOSE: Adequate postoperative pain control following total knee arthroplasty (TKA) is required to achieve optimal patient recovery. However, the postoperative recovery may lead to an unnaturally extended opioid use, which has been associated with adverse outcomes. This study hypothesizes that machine learning models can accurately predict extended opioid use following primary TKA. METHODS: A total of 8873 consecutive patients that underwent primary TKA were evaluated, including 643 patients (7.2%) with extended postoperative opioid use (> 90 days). Electronic patient records were manually reviewed to identify patient demographics and surgical variables associated with prolonged postoperative opioid use. Five machine learning algorithms were developed, encompassing the breadth of state-of-the-art machine learning algorithms available in the literature, to predict extended opioid use following primary TKA, and these models were assessed by discrimination, calibration, and decision curve analysis. RESULTS: The strongest predictors for prolonged opioid prescription following primary TKA were preoperative opioid duration (100% importance; p < 0.01), drug abuse (54% importance; p < 0.01), and depression (47% importance; p < 0.01). The five machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration, and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients. CONCLUSION: The study findings show excellent model performance for the prediction of extended postoperative opioid use following primary total knee arthroplasty, highlighting the potential of these models to assist in preoperatively identifying at risk patients, and allowing the implementation of individualized peri-operative counselling and pain management strategies to mitigate complications associated with prolonged opioid use. LEVEL OF EVIDENCE: IV.

A Bibliometric Analysis of Global Pediatric Emergency Medicine Research Networks.

OBJECTIVES: During the last 3 decades newly formed pediatric emergency medicine (PEM) research networks have been publishing research. A desire of these networks is to produce and disseminate research to improve patient health and outcomes. The aims of the study were to quantitatively analyze and compare the literature by PEM research networks globally through numeric and visual bibliometrics. METHODS: A bibliometric analysis of articles published from 1994 to 2019 (26 years) by authors from PEM research networks globally were retrieved using PubMed, Web of Science (Thompson Reuters), and accessing individual research network databases. Bibliometric analysis was performed utilizing Web of Science, VOSviewer, and Dimensions. Research was quantified to ascertain the number of articles, related articles, citations, and Altmetric attention score. RESULTS: A total of 493 articles were published across 9 research networks in 3 decades. Pediatric Emergency Care Applied Research Network produced the most articles, citations, and h-index of all networks. We identified 3 main groupings of productive authors across the networks who collaborate globally. The sex of the first author was female in 46% of publications, and the corresponding author(s) was female in 45%. A nonsignificant moderate positive correlation between the number of years publishing and the number of publications was identified. There was nonsignificant moderate negative association between the number of countries in a network and total publications per annum. CONCLUSIONS: This study is the first bibliometric analysis of publications from PEM research networks that collaborate globally. Exploring the relationships of numerical bibliometric indicators and visualizations of productivity will benefit the understanding of the generation, reach, and dissemination of PEM research within the global research community.

Predictors of Higher-Order Affective Skills in Health Promotion Among Indonesian Nursing Students: Assessment and Evaluation.

AIM: We examined health-promoting lifestyle in undergraduate nursing students and its potential as a predictor of health-promoting behaviors and basis for curriculum improvement. BACKGROUND: A health-promoting lifestyle is essential among students for their future nursing roles. Development requires the internalization of healthy behaviors beyond the cognitive learning domain. METHOD: Guided by Pender's health promotion model, this multisite, cross-sectional, and descriptive-correlational study surveyed 1,634 undergraduate nursing students in Bali, Indonesia. RESULTS: The regression analysis revealed that student profiles explained the 5 percent variation in their overall health-promoting lifestyle. Perceived health status was observed to be a significant predictor ( p < .05) of health-promoting behaviors among a diverse sample of undergraduate nursing students. CONCLUSION: The study findings emphasize the role of nursing education in instilling healthy behaviors among undergraduate students and the importance of integrating student-centered and context-responsive health promotion strategies into the nursing curriculum.