Real World Research Articles

Putting the Choice in Choice Tasks: Incorporating Preference Elicitation Tasks in Health Preference Research.

Choice-based preference elicitation methods such as the discrete choice experiment (DCE) present hypothetical choices to respondents, with an expectation that these hypothetical choices accurately reflect a 'real world' health-related decision context and that consequently the choice data can be held to be a true representation of the respondent's health or treatment preferences. For this to be the case, careful consideration needs to be given to the format of the choice task in a choice experiment. The overarching aim of this paper is to highlight important aspects to consider when designing and 'setting up' the choice tasks to be presented to respondents in a DCE. This includes the importance of considering the potential impact of format (e.g. choice context, choice set presentation and size) as well as choice set content (e.g. labelled and unlabelled choice sets and inclusion of reference alternatives) and choice questions (stated choice versus additional questions designed to explore complete preference orders) on the preference estimates that are elicited from studies. We endeavoure to instil a holistic approach to choice task design that considers format alongside content, experimental design and analysis.

Panel Interview of ONcology practices with Emergent Experience of teclistamab in the Real world: The TecPIONEER Study.

To understand providers' current teclistamab step-up dosing (SUD) model and how they envision future administration models, as well as perceived barriers and facilitators to these models in day-to-day clinical practice. Interviews of clinicians with RW experience administering teclistamab, with a subsequent roundtable discussion to discuss interview findings. Topics of interest included managing adverse events (AE), and handling logistics of SUD and transition of care (ToC). 20 clinicians representing 19 practices participated. Of 14 practices administering inpatient teclistamab SUD, 12 (86%) utilized a single admission. A day 1-3-5 dosing schedule with a 7-day length of stay was planned in 10/14 (71%). The remaining 5 practices employed outpatient or hybrid SUD. SUD models depended on cellular therapy experience, patient volume, and monitoring capabilities. Clinicians desired to administer SUD outpatient for convenience and reduced healthcare resource use. 11% of practices reported using tocilizumab for cytokine release syndrome (CRS) prophylaxis, whilst it was uniformly used to treat grade 2+ CRS. Corticosteroids were the preferred treatment for neurotoxicity. Infection prophylaxis with intravenous immunoglobulin was reported by 89% of practices. Patient- and institution-level factors affected decision-making of transitioning patients back to referring sites after SUD. The results consolidated practice-based experiences and indicated diverse RW SUD models and patient management strategies in practices with familiarity with teclistamab AE management and ToC protocols. Inpatient SUD is common, with expectations that approaches will evolve toward outpatient or community-based administration. Further research is needed to investigate outcomes of different care models and AE management strategies.

Impact of HIV pre-exposure prophylaxis prescriptions on HIV diagnoses in New York city.

HIV preexposure prophylaxis (PrEP) has proven to be efficacious and effective in preventing HIV infections, but few studies have reported its impact in the real world. We conducted an ecological analysis and compared the trends in HIV PrEP prescriptions with the trends in age-adjusted HIV diagnosis rates in New York City (NYC). Joinpoint regression analyses were used to identify any temporal trends in HIV diagnosis rates in NYC. The number of people filling at least one PrEP prescription in NYC increased from 2551 in 2014 to 35 742 in 2022. The overall age-adjusted HIV diagnosis rate steadily decreased from 48.1 per 100 000 in 2003 to 17.1 per 100 000 in 2022. After the rollout of PrEP, accelerated decreases were detected in some subpopulations including white men [2014-2019 annual percentage change (APC): -16.6%; 95% confidence interval (CI) -22.7 to -10.0], Asian/Pacific Islander men (2016-2022 APC: -9.8%), men aged 20-29 years (2017-2020 APC: -9.4%) and 40 -49 years (2014-2020 APC: -12.2%), Latino/Hispanic people aged 40-49 years (2015-2020 APC: -13.0%), white people aged 20-29 years (2012-2022 APC: -11.4%) and 40-49 years (2014-2018 APC: -27.8%), and Asian/Pacific Islander people aged 20-29 years (2017-2022 APC: -13.0). With a high coverage, PrEP can have a long-term impact in reducing HIV infections in a population, but if preexisting social determinants that contribute to racial, ethnic, and gender inequities are not well addressed, the implementation of PrEP can exacerbate these inequalities.

Uncertain pedestrian load modeling for structural vibration assessment in footbridge design

The request of material savings in bridge constructions leads to the development towards slender and lightweight structures, which are more sensitive to human-induced vibrations caused by walking pedestrians. The resulting accelerations do not endanger the structural safety, but can be perceived as unpleasant for the user and must therefore be restricted within the limit state of serviceability. For this purpose, the guidelines define acceleration intervals, called as “comfort levels” (CL), which evaluate the comfort of the pedestrians, given the maximum acceleration of the bridge. The model for human-induced loads in the design guideline is based on conservative and deterministic simplifications. In particular, the uncertainties in the human gait parameters are neglected. The main objective of this paper is to develop an uncertain load model for walking pedestrians based on Fourier series. The aleatory and epistemic uncertainties in the human gait parameters are quantified with appropriate uncertainty models relying on available data. In a dynamical finite element analysis, the model is used to simulate groups of pedestrians randomly walking over a bridge to calculate the resulting acceleration amplitudes. The approach is applied to a single span beam and to a real world footbridge using a 3D finite element model. The results are then evaluated within the CLs, enabling new assessment methods.

A level playing field? Evaluation of the virtual Objective Structured Clinical Examination in Psychiatry and Addiction Medicine: A mixed methods study.

The Objective Structured Clinical Examination (OSCE) has been used for clinical assessment of a broad range of medical student competencies in Psychiatry and Addiction Medicine. However, there has been little research into online assessments. We investigated the virtual OSCE (v-OSCE) from the user perspective to better understand its acceptability, usefulness, benefits, challenges and potential improvements. At the conclusion of the v-OSCE, all participants (medical students, examiners and simulated patients) were invited to participate in a brief online survey, based on the Technology and Acceptance Model. Freeform qualitative feedback was also obtained to explore participants' experiences and attitudes. Participants reported the v-OSCE was acceptable, efficient, convenient and easy to use. It was perceived as useful for demonstrating students' interviewing skills and interacting with the simulated patient. Benefits included greater convenience, reduced stress and travel time. Challenges were similar to those experienced in 'real world' telepsychiatry, primarily related to assessment of non-verbal cues and emotional prosody. Our findings inform recommendations for improving online examinations. These include increased preparation, practice and professionalism, to better simulate the in-person experience. Study credibility was strengthened by the triangulation of qualitative, quantitative and psychometric data.

SLM-SLAM: A Visual SLAM System Based on Segmented Large-Scale Model in Dynamic Scenes and Zero-shot Conditions

Abstract In the real world, the presence of various dynamic objects can impact the localization accuracy of the majority of typical Visual Simultaneous Localization and Mapping (VSLAM) systems. Simultaneously, many dynamic VSLAM systems based on neural networks require pre-training for specific application scenarios. We present SLM-SLAM, the first VSLAM system that implements zero-shot processing of dynamic scenes. It achieves the capability to handle various dynamic objects without the necessity for pre-training, enabling straightforward adaptation to different application scenarios. Firstly, we designed an open-world semantic segmentation module based on a segmented large-scale model to acquire semantic information in the scene. Subsequently, we devised a label-based strategy for selecting feature points, jointly optimizing poses with the weighted labels provided by both semantic and geometric information. Finally, we refined the keyframe selection strategy of ORB-SLAM3 to prevent matching errors caused by an insufficient number of remaining static feature points in the scene. We conducted experiments on the TUM dataset, the KITTI dataset, and real world scenarios. The results indicate that in dynamic scenes, our SLM-SLAM significantly improves localization accuracy compared to ORB-SLAM3, and its performance is comparable to state-of-the-art dynamic VSLAM systems.

Industry 4.0 in the Perspective of Supply Chain Management: Evolution and Future Research Agenda

With the rise of Industry 4.0 (I4.0), there has been a shift from traditional to digital ways of running businesses by bringing the real and virtual worlds together. This study examines I4.0 literature in Supply chain management (SCM) over the last five years, finds essential research areas and gaps, and suggests a roadmap of new possibilities and opportunities for further research. First, a performance analysis was undertaken in which the most relevant or productive authors, leading journals, and top countries were examined. Second, a science mapping analysis involving citation, co-citation, and keyword analysis was conducted. Third, a thematic analysis was performed in which numerous themes were discussed in the direction of I4.0 in SCM. The authors examined 260 research publications published during the last five years. The authors used the R package bibliometrix, biblioshiny application, and the Vosviewer to visualize the structure of over 19,000 different references. Finally, the paper suggests a framework for implementing I4.0 in the supply chain (SC). The outcome of this study will help academic scholars, industry practitioners and engineering managers to identify significant themes and areas and implement I4.0 standards in SCM.

Real World Outcomes with Treatment Free Remission in Chronic Myeloid Leukemia-Experience from a Tertiary Care Cancer Centre

Real World Outcomes with Treatment Free Remission in Chronic Myeloid Leukemia-Experience from a Tertiary Care Cancer Centre

EEGminer: discovering interpretable features of brain activity with learnable filters

Objective. The patterns of brain activity associated with different brain processes can be used to identify different brain states and make behavioural predictions. However, the relevant features are not readily apparent and accessible. Our aim is to design a system for learning informative latent representations from multichannel recordings of ongoing EEG activity. Approach: We propose a novel differentiable decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. Specifically, we introduce filters parameterized by generalized Gaussian functions that offer a smooth derivative for stable end-to-end model training and allow for learning interpretable features. For the feature module, we use signal magnitude and functional connectivity estimates. Main results. We demonstrate the utility of our model on a new EEG dataset of unprecedented size (i.e. 721 subjects), where we identify consistent trends of music perception and related individual differences. Furthermore, we train and apply our model in two additional datasets, specifically for emotion recognition on SEED and workload classification on simultaneous task EEG workload. The discovered features align well with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening. This agrees with the specialisation of the temporal lobes regarding music perception proposed in the literature. Significance. The proposed method offers strong interpretability of learned features while reaching similar levels of accuracy achieved by black box deep learning models. This improved trustworthiness may promote the use of deep learning models in real world applications. The model code is available at https://github.com/SMLudwig/EEGminer/.

Engaging secondary school students in sustainability assessments through farm nutrient mass balances

AbstractDecision‐support tools (DSTs) are increasingly used to collect, manage, and/or analyze growing amounts of data generated on farms. The whole‐farm nutrient mass balance (NMB) is an assessment with an associated DST (the Cornell NMB calculator) that measures farm nutrient management efficiency. Our main objectives were to (1) develop and implement a secondary school curriculum on NMB assessments; (2) evaluate its impact on student knowledge and perceptions of agricultural sustainability, nutrient management, careers in agriculture, awareness about data collection and use of DSTs, and analytical and quantitative skills; and (3) assess teacher experiences delivering the materials. The curriculum was implemented across 21 New York classrooms. Students completed one pre‐ and one post‐curriculum implementation questionnaire (n = 134). Teachers completed a post‐curriculum implementation questionnaire (n = 19). Results showed that the curriculum enhanced student knowledge of nutrient management and effectively conveyed the fundamentals of the NMB metrics. It also impacted student perceptions of the utilization of DSTs and need for mathematical skills and data management knowledge in agriculture. The curriculum did not influence student interest in pursuing agricultural careers but exposed them to aspects of farming that they were not previously familiar with. The math‐focused lessons were identified as challenging by many students and teachers. Teachers found the curriculum applicable to the real world, exposing students to new aspects of farming. We conclude that real‐world case‐study examples, carefully planned math‐focused lessons, appropriate support materials including widely installable and user‐friendly software, and training opportunities for teachers can help effective teaching of DST use and agriculture sustainability concepts.

AUGMENTED REALITY TECHNOLOGY FOR VISUALIZING DIPANEGARA UNIVERSITY BUILDINGS

Multimedia technology continues to advance, one of which is Augmented Reality (AR) technology, commonly known as the technology for merging virtual objects into the real world. Building brochures, which only display building photos and are visible from only one side, require us to visit the building to view its structure. Therefore, an idea emerged for a design of an application that uses Android-based Augmented Reality technology. This application allows users to view a more realistic physical shape of the building without visiting it directly by directing the camera towards a marker displaying the building's logo and showing a 3D model of the Dipanegara University building. It also provides general information about the building. This application is designed using 3DS Max, Unity, and Vuforia SDK, making it easier for prospective students to view the building's layout. Keywords : 3DS Max, Android, Augmented Reality, Unity, Visualization.

Plasma biomarkers increase diagnostic confidence in patients with Alzheimer’s disease or frontotemporal lobar degeneration

BackgroundThe recent development of techniques to assess plasma biomarkers has changed the way the research community envisions the future of diagnosis and management of Alzheimer’s disease (AD) and other neurodegenerative disorders. This work aims to provide real world evidence on the clinical impact of plasma biomarkers in an academic tertiary care center.MethodsAnonymized clinical reports of patients diagnosed with AD or Frontotemporal Lobar Degeneration with available plasma biomarkers (Aβ42, Aβ42/Aβ40, p-tau181, p-tau231, NfL, GFAP) were independently assessed by two neurologists who expressed diagnosis and diagnostic confidence three times: (T0) at baseline based on the information collected during the first visit, (T1) after plasma biomarkers, and (T2) after traditional biomarkers (when available). Finally, we assessed whether clinicians’ interpretation of plasma biomarkers and the consequent clinical impact are consistent with the final diagnosis, determined after the conclusion of the diagnostic clinical and instrumental work-up by the actual managing physicians who had complete access to all available information.ResultsClinicians assessed 122 reports, and their concordance ranged from 81 to 91% at the three time points. At T1, the presentation of plasma biomarkers resulted in a change of diagnosis in 2% (2/122, p = 1.00) of cases, and in increased diagnostic confidence in 76% (91/120, p < 0.001) of cases with confirmed diagnosis. The change in diagnosis and the increase in diagnostic confidence after plasma biomarkers were consistent with the final diagnosis in 100% (2/2) and 81% (74/91) of cases, respectively. At T2, the presentation of traditional biomarkers resulted in a further change of diagnosis in 13% (12/94, p = 0.149) of cases, and in increased diagnostic confidence in 88% (72/82, p < 0.001) of cases with confirmed diagnosis.ConclusionsIn an academic tertiary care center, plasma biomarkers supported clinicians by increasing their diagnostic confidence in most cases, despite a negligible impact on diagnosis. Future prospective studies are needed to assess the full potential of plasma biomarkers on clinical grounds.

A randomised, crossover trial exploring the patient perspective and effectiveness of biosimilar adalimumab transition: IBD reference and biosimilar adalimumab cross over study (iBaSS).

Patient satisfaction has been positively associated with adherence which is expected to impact outcomes. Although vital for successful implementation of biosimilar medicines, little is known about the patient perspective of transition. The aim of this study was to investigate clinical outcomes and patient experience of transitioning between reference adalimumab and a biosimilar (SB5). iBaSS is a phase IV single-centre, prospective, randomised, single-blind, cross-over study in adult subjects with Crohn's disease. Participants, stable on adalimumab before consent, received 24weeks of treatment with both reference adalimumab and SB5. The primary outcome was the proportion of patients maintaining baseline clinical status throughout each treatment period, with patients' perspective of disease control and treatment satisfaction assessed as secondary outcomes. A total of 112 participants, representative of the heterogeneous patient populations encountered in routine clinical practice, were enrolled. A similar proportion of participants maintained baseline clinical status through each treatment period: 81.8% with reference adalimumab and 79.5% with SB5. Patient reported outcomes (IBD-Control questionnaire (SB5: 15.5; reference adalimumab 15) and TSQM), adverse events and therapeutic drug monitoring remained consistent through both treatment periods, although a higher median injection pain VAS score was noted with SB5 (53/100 versus 6/100 with reference adalimumab). The number of switches undertaken in the study did not impact serum drug concentration or immunogenicity. This study, mimicking real world adalimumab transition, demonstrates that patients undertaking brand transition can be expected to have consistent clinical and satisfaction outcomes. Number 2018-004967-30.

Visual Reinforcement Learning for Dynamic Object Detection

Abstract. Object detection is a widely studied task in computer vision. Current methods often focus on images captured from appropriate viewpoints. However, there is a large disparity between objects observed from different viewpoints in the real world. Dynamic Object Detection (DOD) method automatically adjusts the camera viewpoint in a visual scene to sequentially find optimal viewpoints. Currently, the DOD tasks are usually modeled as a sequential decision-making problem and solved using reinforcement learning methods. Existing approaches face challenges with sparse rewards and training instability. To tackle these issues, we proposed a single-step reward function and a lightweight network, respectively. The single-step reward function, which provides timely feedback, gives an efficient training process for DOD tasks. The lightweight network with few parameters can ensure the stability of the training process. To evaluate the effectiveness of our method, we developed a simulation dataset based on UE4, which consists of 1800 training images and 450 testing images. The dataset includes five object categories: vans, cars, trailers, box trucks and SUVs. Experiments demonstrate that our method outperforms SOTA object detectors on our simulation dataset. Specifically, the average precisions(APs) are improved from 89.1% to 96.0% when using the YOLOv8 object detector.

Autonomous Landing Spot Detection for Unmanned Aerial Vehicles Based on Monocular Vision

Abstract. Enhancing the autonomous landing capability of unmanned aerial vehicles (UAVs) is of great significance for improving their operational efficiency and field survival capabilities. To this end, we propose a real-time autonomous landing spot detection method for UAVs. Firstly, the pose of the UAV at any given time and the initial three-dimensional point cloud of the scene are estimated using simultaneous localization and mapping (SLAM) techniques. Then, since the initial point cloud is sparse and cannot be used for terrain analysis, we generate a voxel-based elevation map of the scene, which can establish interconnectivity among the points. Finally, we propose a shift-box strategy to comprehensively analyze various terrain factors in the elevation map, determine the landing spot for UAVs, and update the landing spot in real time. The UAV flight experiments conducted in the real world have demonstrated the effectiveness and real-time performance of the proposed method.

Real-world outcomes in patients with chronic spontaneous urticaria receiving omalizumab: insights from a clinical practice survey

Objective: Chronic spontaneous urticaria (CSU) is a debilitating inflammatory skin condition, often impacting quality of life. International guidelines recommend omalizumab, an anti-immunoglobulin E antibody, for second-line treatment. Our objective was to understand patient characteristics associated with prescription of omalizumab, and assess real-world outcomes in patients with CSU treated with omalizumab. Methods: We analyzed data from the Adelphi Real World CSU Disease Specific Programme™, a cross-sectional survey with retrospective data collection (December 2020–October 2021) from physicians and patients with CSU in the United States. Results: Data from allergists (n = 45), dermatologists (n = 51), and primary care physicians (PCPs; n = 20) were included. At the time of data collection, one-third of patients were receiving omalizumab (n = 220) and 67% were eligible for but not receiving omalizumab (n = 455). Using logistic regression, the odds of receiving omalizumab were higher in patients whose entire bodies were affected by hives [OR = 2.551; 95% CI 1.502-4.333; p < 0.001] or with deteriorating/unstable prognoses at treatment initiation [OR = 2.219; 95% CI 1.031-4.777; p = 0.042], and lower in patients managed by PCPs [OR = 0.276; 95% CI 0.130-0.584; p < 0.001]. Estimates from an inverse probability weighted regression adjustment model indicated that patients receiving omalizumab had higher treatment satisfaction, improvements in itching, hives, angioedema, insomnia, and anxiety, and lower impact on work productivity, compared with patients not receiving omalizumab. Conclusion: Around two-thirds of patients with CSU considered eligible for omalizumab were not receiving the guideline-recommended therapy. Patients receiving omalizumab had better real-world outcomes compared with patients not receiving omalizumab. Ensuring patients receive the most appropriate treatment could benefit patients with CSU.

The Brain's Sensitivity to Real-world Statistical Regularity Does Not Require Full Attention.

Predictive coding accounts of perception state that the brain generates perceptual predictions in the service of processing incoming sensory data. These predictions are hypothesized to be afforded by the brain's ability to internalize useful patterns, that is, statistical regularities, from the environment. We have previously argued that the N300 ERP component serves as an index of the brain's use of representations of (real-world) statistical regularities. However, we do not yet know whether overt attention is necessary in order for this process to engage. We addressed this question by presenting stimuli of either high or low real-world statistical regularity in terms of their representativeness (good/bad exemplars of natural scene categories) to participants who either fully attended the stimuli or were distracted by another task (attended/distracted conditions). Replicating past work, N300 responses were larger to bad than to good scene exemplars, and furthermore, we demonstrate minimal impacts of distraction on N300 effects. Thus, it seems that overtly focused attention is not required to maintain the brain's sensitivity to real-world statistical regularity. Furthermore, in an exploratory analysis, we showed that providing additional, artificial regularities, formed by altering the proportions of good and bad exemplars within blocks, further enhanced the N300 effect in both attended and distracted conditions, shedding light on the relationship between statistical regularities learned in the real world and those learned within the context of an experiment.

Digital twins in aircraft production and MRO: challenges and opportunities

AbstractThe digital twin (DT) concept, value-adding connecting the real and digital world, has been a rising trend in recent years, while the implementation and observation of challenges are still subject to research. Implementations of holistic Digital Twins of tangible and intangible assets of complex products or processes are often ideal-theoretic; instead, only subsystems and processes are replicated, which digital representations serve specific, meaningful applications. Specifically, with its distinct characteristics, the aviation industry and its production show various future application scenarios, which we use case-driven outline in this work. Therefore, we first summarize common, industry-neutral challenges of implementing Digital Twins and give an overview of aircraft production characteristics. Then, we will outline different fields of utilizing the Digital Twin concept and highlight integrational, organizational, and compliance-related challenges as well as opportunities in the context of aircraft production and Maintenance, Repair, and Overhaul (MRO). The use cases are located at different aircraft life cycle phases, from production system development, production supplying logistics, and Quality Assurance (QA) up to retrofit.

Prevalence of omalizumab resistant chronic urticaria and real world effectiveness of dupilumab in omalizumab refractory chronic urticaria patients: a single center experience.

Chronic urticaria (CU) is characterized by wheals, angioedema, or both lasting for ≥ 6 weeks with chronic spontaneous urticaria (CSU) being the most common subtype. Omalizumab-resistant CSU cases represent an unmet clinical need. In this study, we aimed to assess the prevalence and predictors of omalizumab failure in a large cohort of CU patients and assess the effectiveness of dupilumab for omalizumab-recalcitrant CU. Of 338 CU patients, 33 received omalizumab. 69.7% (23 patients) were responders and 30.3% (10 patients) non-responders. Bivariate regression demonstrated that female sex (adjusted OR [aOR] = 1.53; 95%CI = 1.14-2.06), higher baseline UAS7 (aOR = 1.05; 95%CI = 1.01-1.09) and older age (controlling for sex) (aOR = 1.00; 95%CI = 1.00, 1.01) were associated with omalizumab failure. Of 10 omalizumab-refractory patients, three were well controlled with cyclosporine (all children), whereas the seven adults failed on average 5.6 ± 2.6 therapies including cyclosporine. All 7 achieved a complete response with dupilumab with time to response varying between 1 to 6 months. While our results suggest a favourable efficacy of dupilumab omalizumab-resistant cases, future confirmatory studies are required.

Automated Determination of Music Genre and Linguistic Categorization

The main objective of the project is to build a system that allows users to predict genre as well as language out of some music . The Automated determination of musical genre and linguistic categorization is a machine learning based prediction model which is used to predict the genres and languages of an audio . Musical genres are categorical labels created by humans to characterize piece of music. A musical genre is characterized by common characteristics typically related to the instrumentation, rhythmic structure and harmonic content of the music. The performance and relative importance of the proposed features are investigated by training the statistical pattern recognition classifiers using real world audio collections and using machine learning algorithm.In this project, we built systems called music genre and language classification and the effects of different feature extraction methods and their various combinations were also investigated. This task is of great interest in music information retrieval and recommendation systems. In recent years, several machine learning algorithms have been developed to tackle this task, using features such spectral, temporal, and tonal characteristics of audio signals. Music genre prediction and language classification is a task that involves automatically predicting a piece of music into a specific genre category as well as language based on its audio features. Keywords-machine-learning,music,music genre and language classification