Real World Research Articles

Effectiveness of the varicella vaccine in the real world, a matched case-control study

BackgroundUniversal varicella vaccination has been introduced in many countries, but there are a number of important differences in their vaccination strategies. It is essential to establish a vaccination program that can maximize the benefits of varicella vaccine, but there is a lack of comprehensive research on the effectiveness of varicella vaccine in different vaccination status. MethodsUsing data from population-based surveillance platforms we conducted a 1:2 matched case-control study. The cases were clinically diagnosed varicella with onset from 2017 to 2021, 1–14 years old in Chaoyang District, Beijing. The controls were matched according to date of birth (±1 month), sex and residence. The vaccination data of the subjects were obtained from the Childhood Immunization Information Management System in Beijing. Using conditional logistic regression models with or without interaction terms, we evaluated the effectiveness of varicella vaccine in different vaccination status. ResultsA total of 2528 cases and 5056 controls were enrolled. This study found that whether the time since last vaccination was adjusted had a substantial effect on the comparing vaccine effectiveness (VE) between subgroups. After adjustment for the time since last vaccination, 1) the incremental VE of 2-dose was 49.6 % (95 % Confidence Interval [CI], 38.8–58.6) compared with 1-dose (93.9 % vs. 88.0 %); 2) Among children who received one dose, the risk of chickenpox in children vaccinated at 18–23 months was 1.382 (95 %CI, 1.084–1.762) times that in children vaccinated at 12–17 months. 3) the VE with less than one, two, and three year intervals is higher than that with six-year-intervals (P < 0.05), respectively. ConclusionsWhen comparing VE between subgroups of different vaccination status, the time since last vaccination should be adjusted. The first dose of varicella vaccine should be given as early as the second year of life, and the second dose can improve vaccine effectiveness.

Influence maximization for heterogeneous networks based on self-supervised clustered heterogeneous graph transformer

Influence maximization (IM) has drawn significant attention in recent years. Most existing IM methods primarily focus on homogeneous networks, and do not take into account the heterogeneity and the attributes of different types of nodes in heterogeneous networks. However, heterogeneous networks are ubiquitous in real world, encompassing rich semantics and complex structural information. Additionally, the clustering characteristics inherent in a network have a critical and substantial impact on the process of information diffusion, which is often overlooked in IM models designed for heterogeneous networks. To address the challenges posed by the heterogeneity and clustering structure in heterogeneous networks, we propose a novel deep learning framework based on a self-supervised clustered heterogeneous graph transformer for IM in heterogeneous networks, which we have named SCHGT-IM. SCHGT-IM aggregates the heterogeneity and clustering information in heterogeneous networks and incorporates a clustered cascade (CC) model as an information diffusion model to enhance the realism of simulations. We evaluate the performance of SCHGT-IM in comparison with that of state-of-the-art IM models using three academic heterogeneous networks extracted from the DBLP dataset. The experimental results on influence spread demonstrate that SCHGT-IM is superior to fourteen state-of-the-art algorithms and is highly effective in selecting influential seed nodes of different types from heterogeneous networks.

Real World Application of WIfI Scores in Peripheral Arterial Disease Patients

The Society for Vascular Surgery wound, ischemia, and foot infection (WIfI) classification system aims to risk stratify patients with chronic limb threatening ischemia (CLTI), predicting both amputation rates and the need for revascularization. However, real-world utilization of the system and whether it accurately predicts outcomes following open revascularization and peripheral interventions is unclear. Therefore, we sought to determine the adoption of the WIfI classification system within a contemporary statewide collaborative as well as the impact of patient factor, and WIfI risk assessment on short- and long-term outcomes. Using data from a large statewide collaborative, we identified patients with CLTI undergoing open surgical revascularization or peripheral vascular intervention (PVI) between 2016 - 2022. The primary exposure was preoperative clinical WIfI stage. Patients were categorized according to the SVS Lower Extremity Threatened Limb Classification System into clinical WIfI stages1, 2, 3, or 4. The primary outcomes were 30-day and 1-year amputation and mortality. Multivariable logistic regression was performed to estimate the association of WIfI stage on post-revascularization outcomes. In the cohort of 17,417 patients, 83.4% (n=14,529) had WIfI stage documented. Peripheral vascular interventions (PVIs) were performed on 57.6% of patients, and 42.4% underwent an open surgical revascularization (OSR). 49.5% of patients were classified as stage 1, 19.3% stage 2, 12.8% stage 3 and 18.3% of patients met stage 4 criteria. Stage 3 and 4 patients had higher rates of diabetes, congestive heart failure, and renal failure, and were less likely to be current or former smokers. One-half of stage 3 patients underwent OSR, while stage 1 patients were most likely to have received a PVI (64%). As WIfI stage increased from 1 to 4, 1-year mortality increased from 12% to 21% (p<0.001), 30-day amputation rates increased from 5% to 38% (p<0.001), and 1-year amputation rates increased from 15% to 55% (p<0.001). Finally, patients who did not have WIfI scores classified had significantly higher 30-day and 1-year mortality, as well as higher 30-day and 1-year amputation rates. The Society for Vascular Surgery WIfI clinical stage is significantly associated with 1-year amputation rates in patients with CLTI following lower extremity revascularization. As nearly 55% of stage 4 patients require a major amputation within one year of intervention, this study supports use of the WIfI classification system in clinical decision making for patients with CLTI.

Aesthetic flat closure in the real world: Myth or reality - A qualitative study of healthcare professionals’ opinions of the quality of mastectomy scars when no reconstruction has been performed

Aesthetic flat closure in the real world: Myth or reality - A qualitative study of healthcare professionals’ opinions of the quality of mastectomy scars when no reconstruction has been performed

A learning numerical spiking neural P system for classification problems

In recent years, classification problems have been widely studied as one of the critical research directions in artificial intelligence. Efficiently simulating the human brain’s ability to solve deterministic classification problems has become a prominent research branch in the field of classification problems. Numerical spiking neural P systems (NSN P systems) with the characteristics of the highly flexible spiking neural P (SNP)systems architecture and addressing numerical variants have special advantages for solving classification problems in the real world. This paper proposes, a novel NSN P system, namely, a learning numerical spiking neural P system (LNSN P system), is proposed to solve classification problems. In LNSN P systems, each neuron contains not only discrete spikes and rules that must be described by regular expressions but also numerical variables and production functions that can be in any form, overcoming the discontinuity of traditional SN P systems. In addition, an ascending structure without an outer encoder has been designed in this study to encode information from low-dimensional indivisible space to high-dimensional divisible space. At the same time, an adaptive learning mechanism has been introduced to improve algorithm convergence and precision. Finally, the experimental results obtained using eight benchmark datasets from the machine learning repository demonstrate the feasibility and effectiveness of the proposed LNSN P system. Moreover, the proposed LNSN P system exhibits lower space complexity and better generalisation ability than the existing SN P systems with the learning ability to solve different classification problems.

Multimodality Driven Impedance-Based Sim2Real Transfer Learning for Robotic Multiple Peg-in-Hole Assembly.

Robotic rigid contact-rich manipulation in an unstructured dynamic environment requires an effective resolution for smart manufacturing. As the most common use case for the intelligence industry, a lot of studies based on reinforcement learning (RL) algorithms have been conducted to improve the performances of single peg-in-hole assembly. However, existing RL methods are difficult to apply to multiple peg-in-hole issues due to more complicated geometric and physical constraints. In addition, previously limited solutions for multiple peg-in-hole assembly are hard to transfer into real industrial scenarios flexibly. To effectively address these issues, this work designs a novel and more challenging multiple peg-in-hole assembly setup by using the advantage of the Industrial Metaverse. We propose a detailed solution scheme to solve this task. Specifically, multiple modalities, including vision, proprioception, and force/torque, are learned as compact representations to account for the complexity and uncertainties and improve the sample efficiency. Furthermore, RL is used in the simulation to train the policy, and the learned policy is transferred to the real world without extra exploration. Domain randomization and impedance control are embedded into the policy to narrow the gap between simulation and reality. Evaluation results demonstrate the effectiveness of the proposed solution, showcasing successful multiple peg-in-hole assembly and generalization across different object shapes in real-world scenarios.

Lomitapide: A Medication Use Evaluation and a Formulary Perspective.

Lomitapide is approved for lowering low-density lipoprotein cholesterol (LDL-C) in homozygous familial hypercholesterolemia, which is a rare genetic disorder. The evidence regarding its safety and efficacy from a small clinical trial requires further validation for effectiveness and safety in the real world. This study aimed to use institutional data on the effectiveness and safety of lomitapide to assist in formulating a perspective on adding it to the formulary. This was a retrospective review of patients who were actively prescribed lomitapide at King Abdulaziz Medical City, Riyadh, Saudi Arabia, from 2019 to 2022. Data collection included demographics, confirmed gene mutation results, duration of lomitapide therapy, baseline, on-treatment, last LDL-C levels, percent reduction in LDL-C after 1-3 months of therapy (whichever was first available), other LDL-C lowering therapies used, liver function tests, adverse effects, and compliance. Eight adult patients were included in the review, with a mean age of 25.5 years. Approximately 75% were female, and the duration of treatment with lomitapide ranged from 9 months to 3 years. None of the patients were on continuous LDL apheresis. The mean baseline LDL-C at presentation to our facility was 17.2 mmol/L (range, 11.78-21.97 mmol/L), the mean percent drop in LDL-C with lomitapide was 34.1% (range, 0%-87%), gastrointestinal disturbances were documented in 50% of the patients, and no cases of severe liver toxicities or increase in liver enzymes were seen. In our cohort of adult patients, lomitapide showed an overall modest reduction in LDL-C, with no cases of increase in liver enzymes and documented intolerance, indicating that most patients were likely noncompliant. This review revealed important considerations when reimbursing expensive medications for rare diseases. Real-world evidence in real-time can support healthcare systems in price negotiations and reaching mutual agreements that can eventually improve patient access to care.

Analisis Syariah Marketing Mix Arrum BPKB dan Urgensinya dalam Meningkatkan Pendapatan UMKM Nasabah Pegadaian UPS Diponegoro Sumenep

The main objective of this study is to analyse the impact of the Arrum BPKB sharia marketing mix at Pegadaian UPS Diponegoro on the income level of MSME customers.This qualitative case study uses structured interviews to collect primary data from MSME players, complemented by secondary data from institutional records. The focus of the analysis is placed on understanding how the application of Sharia principles in marketing can improve business funding solutions. The application of Sharia Marketing Mix for Arrum BPKB financing has significantly increased customer interest and participation. Interviews with five MSME customers showed a marked increase in their daily and monthly income, confirming the effectiveness of this strategy in the real world. This study highlights the alignment of Shariah-compliant practices with customer needs and market dynamics. The findings underscore the importance of incorporating Shariah principles in marketing strategies in Islamic financial services. In practical terms, this approach not only helps in complying with ethical standards, but also enhances customer trust and financial inclusion for cash-strapped MSMEs. This study introduces new insights into the localised impact of Shariah marketing practices on MSME profitability, particularly in the Indonesian financial services context, and sets a precedent for further regional studies

Localization of small moving objects using only total fields in microwave imaging

Microwave imaging for object localization utilizes the differences in scattering parameter data measured with and without the object of interest. However, in the real world, it is often impossible to measure incident fields in the background medium without the object. This contribution presents a method to estimate scattered field data through simple statistical processing of the measured total field data and apply it to reconstruct localized images of small moving objects. The effectiveness of the proposed method was experimentally verified.

Paxlovid TM reduces the incidence of pneumonia, hospitalization, and death in a cohort of COVID-19 patients from northeast Mexico

BackgroundInformation on PaxlovidTM effectiveness must be monitored and updated in real world scenarios. Our research question was what is the effectiveness of PaxlovidTM in adult patients with COVID-19? Therefore, we investigated the effectiveness of PaxlovidTM on reducing the incidence of pneumonia, hospitalization, and mortality in a cohort of COVID-19 positive adult patients from northeast Mexico. MethodsA retrospective cohort study of COVID-19 positive adult patients from Nuevo Leon, Mexico from December 2020 to May 2023 (after Omicron BA-5 circulation) was performed. Paxlovid™ use was authorized in September 2022. Therefore, we analyzed effectiveness in patients with confirmed diagnosis who met selection criteria between September 2022 and May 2023 (n = 20,799; 5,673 with and 15,126 without PaxlovidTM). ResultsThe pneumonia (0.1% vs. 0.4%, p < 0.0001), hospitalization (0.1% vs. 1.2%, p < 0.0001), and death rates (0.04% vs. 0.2%, p < 0.0001) were lower in patients with PaxlovidTM treatment independently of age, sex, comorbidity, and COVID-19 and pneumococcal vaccination history. Effectiveness was 88.2%, 95.9% y 91.9% for pneumonia, hospitalization, and death, respectively. ConclusionsPaxlovidTM reduces the presentation of pneumonia, hospitalization, and death secondary to COVID-19. It is recommended to continue monitoring PaxlovidTM effectiveness, as other SARS-CoV-2 variants continue to emerge.

Unraveling the impact of COVID-19 vaccination on hospital stay, ICU admission and mortality: A real world evidence from Pakistan

Unraveling the impact of COVID-19 vaccination on hospital stay, ICU admission and mortality: A real world evidence from Pakistan

C-29 | Utility of invasive bolus thermodilution method in diagnosis of coronary microvascular dysfunction (CMD): Real World data

C-29 | Utility of invasive bolus thermodilution method in diagnosis of coronary microvascular dysfunction (CMD): Real World data

Cost, Efficiency and Outcomes of Pulsed Field Ablation versus Thermal Ablation for Atrial Fibrillation: A Real World Study

BackgroundWith the exponential growth of catheter ablation (CA) for atrial fibrillation (AF), there is increasing interest in associated healthcare costs. Pulsed field ablation (PFA) using a single-shot pentaspline multi-electrode catheter has been shown to be safe and effective for AF CA, but its cost efficiency compared to conventional thermal ablation modalities (cryoballoon [CB] or radiofrequency [RF]) has not been evaluated. ObjectiveTo compare cost, efficiency, effectiveness and safety between PFA, CB and RF for AF ablation. MethodsWe studied 707 consecutive patients (208 PFA, 325 CB, 174 RF) undergoing first-time AF ablation. Individual procedural costs were calculated, including equipment, lab utilisation and hospital stay, and compared between ablation modalities, as effectiveness and safety. ResultsSkin-to-skin times and catheter lab times were significantly shorter with PFA (68min/102min) vs CB (91min/122min) and RF (89min/123 min); p<0.001. General anaesthesia utilisation differed across modalities (PFA 100%, CB 10.2%, RF 61.5%); p<0.001. Major complications occurred in 1% of cases, with no significant differences between modalities. Shorter procedural times resulted in lower staffing and lab costs with PFA, but these savings were offset by substantially higher equipment costs, resulting in higher overall median costs with PFA (£10,010) vs CB (£8,106) and RF (£8,949); p<0.001. ConclusionIn this contemporary real-world study of the three major AF CA modalities used concurrently, PFA had shorter skin-to-skin and catheter lab times than CB and RF, with similarly low rates of complications. However, PFA procedures were considerably more expensive, largely due to higher equipment cost.

Booster effects and mechanisms of web-based personalised normative feedback alcohol intervention for college students: a pragmatic randomised controlled trial

BackgroundTo evaluate the effects of booster and no booster versions of web-based alcohol Personalised Normative Feedback (PNF) and whether descriptive norms mediated and/or participant motivation moderated the effectiveness of the intervention in real world conditions (i.e. no financial incentives). MethodsPragmatic randomised controlled trial with 1-, 3-, and 6-month assessments. Brazilian college students reporting alcohol use in the last 12 months (N=931) were recruited from May/2020 to December/2022 and allocated to 1) No booster/single PNF(S-PNF); 2) Booster/multiple PNF(M-PNF); or 3) Assessment-only control. We applied Helmert coding [1: Any intervention (S-PNF or M-PNF) vs. Control; and 2: S-PNF vs. M-PNF]. Primary outcomes: typical number of drinks/week and maximum number of drinks/week; secondary outcomes: drinking frequency and number of consequences. Three-months assessment was the primary interval. Descriptive norms were tested as mediator. Interest, importance, and readiness to change were examined as moderators. ResultsCompared to control, any intervention did not influence primary outcomes at 3-months or 6-months, but did at 1-month, when reduced typical drinking (IRR:0.77, 95%CI:0.66;0.90) and maximum number of drinks (IRR:0.69, 95%CI:0.58;0.82). There was an intervention effect on the consequences at 3-months. No differences were observed between S-PNF and M-PNF. No mediation effects were found at 3-months. At 6-months, there was an indirect effect on typical drinking through norms at 3-months (b=-0.82, 95%CI:-2.03;-0.12) and effects on maximum drinks through norms at 1-month (b=-0.54, 95%CI:-1.65;-0.02). No support for moderation was found. ConclusionsIntervention reduced alcohol drinking at 1 month only and was not effective thereafter. Mechanisms of effect remain unclear.

Leveraging cascading information for community detection in social networks

Information exchange among individuals is one of the key factors leading to formation of modular structures in social networks, often referred as communities. In this paper, we devise a novel information diffusion based approach to leverage the latent information exchange among the individuals for identifying communities. Designed an algorithm called PraSar, which has mainly two phases namely seeding and unification. In seeding phase, closely connected groups of nodes called seed communities are identified by analyzing the cascades obtained during information diffusion. In unification phase, the external connections of seed communities are reduced by unifying two or more seed communities. Involvement of cascade that are formed during the diffusion process enables PraSar to operate locally and giving an overall complexity of O(n2). The theoretical foundation of the proposed approach is established by various theorems. Empirical results on diverse real world datasets are evident for the effectiveness and competitiveness of the PraSar algorithm over state-of-the-art community detection algorithms.

First Digit Distributions of Gamma-Ray Bursts

The occurrence of the first significant digits from real world sources is usually not equally distributed, but is consistent with a logarithmic distribution instead, known as Benford’s law. In this work, we perform a comprehensive investigation on the first digit distributions of the duration, fluence, and energy flux of gamma-ray bursts (GRBs) for the first time. For a complete GRB sample detected by the Fermi satellite, we find that the first digits of the duration and fluence adhere to Benford’s law. However, the energy flux shows a significant departure from this law, which may be due to the fact that a considerable part of the energy flux measurements is restricted by lack of spectral information. Based on the conventional duration classification scheme, we also check if the durations and fluences of long and short GRBs (with duration T 90 > 2 s and T 90 ≤ 2 s, respectively) obey Benford’s law. We find that the fluences of both long and short GRBs still agree with the Benford distribution, but their durations do not follow Benford’s law. Our results hint that the long–short GRB classification scheme does not directly represent the intrinsic physical classification scheme.

Informed Adaptive Sensing.

For many inverse problems, the data on which the solution is based is acquired sequentially. We present an approach to the solution of such inverse problems where a sensor can be directed (or otherwise reconfigured on the fly) to acquire a particular measurement. An example problem is magnetic resonance image reconstruction. We use an estimate of mutual information derived from an empirical conditional distribution provided by a generative model to guide our measurement acquisition given measurements acquired so far. The conditionally generated data is a set of samples which are representative of the plausible solutions that satisfy the acquired measurements. We present experiments on toy and real world data sets. We focus on image data but we demonstrate that the method is applicable to a broader class of problems. We also show how a learned model such as a deep neural network can be leveraged to allow generalisation to unseen data. Our informed adaptive sensing method outperforms random sampling, variance based sampling, sparsity based methods, and compressed sensing.

Design for the real world: a problem-based learning approach

AbstractProblem-based Learning is an established learner-centred pedagogical approach for developing skills and competencies. This paper presents the evolutionary development of a Massive Open Online Course, titled "Design for the Real World: A Problem-based Learning Approach", with the aim to empower learners to independently pursue problem-based inquiry using design methods and tools, thereby fostering creativity and life-long learning.

In search of an appropriate front-of-package nutritional labelling scheme for the CARICOM region

PurposeThis study aims to evaluate the impact of the proposed CARICOM Octagon “High In” Warning Label (OWL), against four alternative Front-of-Package Nutrition Labels (FOPNLs): US “Facts Up Front” (FUF), UK Multi-Traffic Light (MTL), Mexican OWL and the Brazilian Magnifying Glass “High In” Warning Label, on respondents’ purchase intentions, perception of healthiness and understanding of nutritional information across and within food products.Design/methodology/approachIn an online randomized control experiment, adults from eight CARICOM countries (n = 948) were randomly assigned to a control and four treatment FOPNL groups. Respondents were tasked to choose between four categories of mock products with three variations in healthfulness across and within products.FindingsNo statistically significant difference (p > 0.05) was found across FOPNL groups compared to the OWL on outcomes of purchase intentions and perception of healthfulness. Regarding the understanding of nutritional information, FUF performed the best, as participants were 1.76 times (p = 0.03) and 3.23 times (p = 0.00) more likely to correctly identify the products with the highest and lowest amount of sugar, respectively. Results were similar for products with the lowest sodium (odds ratio [OR] = 2.25, p = 0.00) and highest saturated fats (OR = 2.11, p = 0.00).Research limitations/implicationsSome limitations of this study include the use of an online platform to conduct the experiment. Though this was the most cost-effective method of execution and presented many benefits, there were limitations to using this approach. Firstly, this approach may not entirely replicate the real world in store purchasing settings. Although online grocery shopping is becoming increasingly popular, in the Caribbean, most grocery purchases are made in stores. Furthermore, online surveys are more likely to lead to samples with higher educational and income levels than the average population (Bethlehem, 2010). The skewedness observed was not unique to this study and was common with similar published studies (Franco-Arellano et al., 2020; Packer et al., 2021; Talati et al., 2018). Nevertheless, all respondents were randomly assigned to groups, and it was confirmed that there were no systematic differences in the education and income levels of participants across the FOPNL groups.Practical implicationsSome CARICOM policy makers advocate for the use of “High In” warning labels to limit the intake of nutrients of concern (NOCs) and to encourage healthier eating habits among consumers. However, regional private sector stakeholders have expressed concern about the lack of sufficient research undertaken at a regional level, to inform the effectiveness of this model within CARICOM, and some have also expressed a preference for the use of other interpretative and reductive FOPNLs, already in use in the Region. The results of this study reveal that while interpretative FOPNLs like the Draft CARICOM Regional Standard, DCRS5 (OWL) can assist consumers in making healthier purchase decisions, it was outperformed by the MTL on perception of healthiness and by the FUF on the understanding of nutrient information. It was also noted that the DCRS5 (OWL) was more effective when choosing across products with distinct nutritional differences but performed poorly in assisting respondents with making healthy purchasing decisions when all the products contained NOCs above the relevant thresholds. This study’s findings highlight that the existing FOPNL schemes can be further enhanced for improved outcomes. This can be achieved by using a hybrid approach which includes both reductive and interpretative elements to allow for comparison across and within food products. The literature also suggests the use of colour and combining positive as well as negative elements to encourage ease of interpretation, improved understanding and healthier food choices.Social implicationsA properly designed FOPNL can support consumers in making healthier food choices; however, it must be accompanied by measures to raise consumer awareness and increase the health literacy of the population to cause shifts in preferences and behavioural patterns over time. This must also be coupled with policies to make healthy food choices more affordable to the general population.Originality/valueThe results of this study revealed that FUF and MTL performed the best in assisting participants to correctly identify between products with the highest or lowest NOCs at the 5% significant level, and that the OWL performed poorly in assisting participants with making healthy purchasing decisions when all the products contained NOCs above the relevant thresholds.

Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices

Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices