bluuebunny/crossref_metadata_2025_split huggingface dataset:
Virtual reality based rehabilitation in adults with chronic neck pain: a systematic review and meta-analysis of randomized clinical trials
Authors: Brea-Gómez Beatriz , Laguna-González Alejandro , Pérez-Gisbert Laura , Valenza Marie Carmen , Torres-Sánchez Irene 
Published: March 2024

AbstractChronic neck pain is one of the most frequent musculoskeletal disorders, with high prevalence worldwide. Rehabilitation is an essential component of therapeutic strategy. Virtual reality based rehabilitation (VRBR) is a powerful distraction technique that could be beneficial for chronic neck pain patients. The objective of this systematic review was to analyse the effectiveness of VRBR in chronic neck pain treatment. We followed the PRISMA guidelines and used four databases (CINAHL, Medline (Via PubMed), Scopus and Web of Science) from their inception to August 2023. Eligibility criteria were established using PICOS. Methodological quality was evaluated with the Downs and Black scale and the risk of bias with the Revised Cochrane risk-of-bias tool. The meta-analysis was performed using the RevMan software. Six studies were included in the systematic review and the meta-analysis. We observed significant differences in favour of VRBR for pain intensity (SMD =  − 0.46; 95% CI =  − 0.74, − 0.19; p = 0.001), disability (MD =  − 2.84; 95% CI =  − 4.23, − 1.45; p < 0.0001), global perceived effect (MD = 0.49; 95% CI = 0.25, 0.72; p < 0.0001) and patient satisfaction (MD = 0.62; 95% CI = 0.38, 0.86; p < 0.00001). However, at short-term follow-up significant differences were only obtained for disability (MD =  − 3.52; 95% CI =  − 5.85, − 1.20; p = 0.003). VRBR can significantly improve pain intensity, disability, global perceived effect and patient satisfaction. The small number of articles included in the analysis is a limitation, even considering the good methodological quality of these studies. Investigating the effects of VRBR on mid and long-term follow-up and exploring different types of VR are needed.PROSPERO database, registration number ID: CRD42020222129.
New proxemics in new space: proxemics in VR
Authors: Kim Ikhwan , Sung Junghan 
Published: March 2024

AbstractWith the development of computer technology, it is possible to design virtual reality (VR) media that provides services to multiple users. Hall’s proxemics theory, which holds that the distance varies depending on the relationship between people, has been applied when designing VR in such media. However, this concept was usually applied to designs without criticism and without confirming whether proxemic distances established in physical space are equally valid in VR. This study investigated how proxemics in VR activate differently from those in a physical space. We measured the distance and the number of instances of direct contact between people, with 69 participants from Korea and Turkiye. As a result, a proxemics pattern similar to that of a physical space appeared in VR. However, the average distance between participants in the VR was about 160% greater than in the physical space. Also, we could observe direct contact up to 260% more in the VR than in the physical space. We analyzed the collected data using Bayesian ANOVA and t-tests. We could clarify the difference between the two proxemics in physical space and VR, but the reason for the phenomenon has yet to be discovered. However, this study is meaningful because any industry designing VR, such as those in digital games, can directly apply the findings to manipulate multiple users’ emotions and experiences more efficiently. Additionally, this study provides directions for any future studies discussing VR design.
Prognostic modeling in early rheumatoid arthritis: reconsidering the predictive role of disease activity scores
Authors: Bird Alix , Oakden-Rayner Lauren , Smith Luke A. , Zeng Minyan , Ray Shonket , Proudman Susanna , Palmer Lyle J. 
Published: March 2024

Abstract
Objective
In this prospective cohort study, we provide several prognostic models to predict functional status as measured by the modified Health Assessment Questionnaire (mHAQ). The early adoption of the treat-to-target strategy in this cohort offered a unique opportunity to identify predictive factors using longitudinal data across 20 years.

Methods
A cohort of 397 patients with early RA was used to develop statistical models to predict mHAQ score measured at baseline, 12 months, and 18 months post diagnosis, as well as serially measured mHAQ. Demographic data, clinical measures, autoantibodies, medication use, comorbid conditions, and baseline mHAQ were considered as predictors.

Results
The discriminative performance of models was comparable to previous work, with an area under the receiver operator curve ranging from 0.64 to 0.88. The most consistent predictive variable was baseline mHAQ. Patient-reported outcomes including early morning stiffness, tender joint count (TJC), fatigue, pain, and patient global assessment were positively predictive of a higher mHAQ at baseline and longitudinally, as was the physician global assessment and C-reactive protein. When considering future function, a higher TJC predicted persistent disability while a higher swollen joint count predicted functional improvements with treatment.

Conclusion
In our study of mHAQ prediction in RA patients receiving treat-to-target therapy, patient-reported outcomes were most consistently predictive of function. Patients with high disease activity due predominantly to tenderness scores rather than swelling may benefit from less aggressive treatment escalation and an emphasis on non-pharmacological therapies, allowing for a more personalized approach to treatment.

Key Points• Long-term use of the treat-to-target strategy in this patient cohort offers a unique opportunity to develop prognostic models for functional outcomes using extensive longitudinal data.• Patient reported outcomes were more consistent predictors of function than traditional prognostic markers.• Tender joint count and swollen joint count had discordant relationships with future function, adding weight to the possibility that disease activity may better guide treatment when the components are considered separately.
Acute treatment of migraine: quantifying the unmet need through real-world data in Italy
Authors: Sacco Simona , Di Ciaccio Sonia , Di Virgilio Roberto , Pegoraro Valeria , Ornello Raffaele 
Published: March 2024

Abstract
Objective
This study is describing subjects with migraine interrupting or not receiving triptans for acute treatment and providing a national-level estimate of people who might benefit from different therapeutic approaches.

Methods
This is a retrospective analysis using IQVIA Longitudinal Patient Database. Starting from 18 + years old individuals with migraine, we selected two cohorts: subjects with triptans prescriptions before and no triptans prescriptions after Index Date (triptan withdraw) and subjects without triptans prescriptions both before and after Index Date (no triptan prescriptions). Index Date was the first record of a health encounter for migraine in 2019. Individuals with cardiovascular disease (CVD) within no triptan prescriptions group were also quantified.

Results
Triptan withdraw and no triptan prescriptions cohorts numbered 605 and 3270, respectively, 5% and 29% of subjects with migraine. Mean age was 47 and 51 years respectively; women were more represented (~ 80%). Hypertension and thyroid disease were most frequent comorbidities; non-steroidal anti-inflammatory drugs were among most frequently recorded treatments. Subjects with CVD within no triptan prescriptions cohort were 621 and with triptan withdraw cohort subjects represented the basis to estimate those who might benefit from alternative options for the acute treatment of migraine, who were around 60,000 and accounted for 11% of subjects seeking primary care due to migraine.

Conclusions
This analysis provides a real-word estimate of Italian people that might benefit from different therapeutic approaches as an alternative to triptans, which sometimes might be not effective and/or poorly tolerated. Such estimate should be intended as the lower limit of a wider range due to strict criteria adopted.
Machine learning in risk prediction of continuous renal replacement therapy after coronary artery bypass grafting surgery in patients
Authors: Zhang Qian , Zheng Peng , Hong Zhou , Li Luo , Liu Nannan , Bian Zhiping , Chen Xiangjian , Wu Hengfang , Zhao Sheng 
Published: March 2024

Abstract
Objectives
This study aimed to develop machine learning models for risk prediction of continuous renal replacement therapy (CRRT) following coronary artery bypass grafting (CABG) surgery in intensive care unit (ICU) patients.

Methods
We extracted CABG patients from the electronic medical record system of the hospital. The endpoint of this study was the requirement for CRRT after CABG surgery. The Boruta method was used for feature selection. Seven machine learning algorithms were developed to train models and validated using 10 fold cross-validation (CV). Model discrimination and calibration were estimated using the area under the receiver operating characteristic curve (AUC) and calibration plot, respectively. We used the SHapley Additive exPlanations (SHAP) method to illustrate the effects of the features attributed to the model and analyze the effects of individual features on the output of the mode.

Results
In this study, 72 (37.89%) patients underwent CRRT, with a higher mortality compared to those patients without CRRT. The Gaussian Naïve Bayes (GNB) model with the highest AUC were considered as the final predictive model and performed best in predicting postoperative CRRT. The analysis of importance revealed that cardiac troponin T, creatine kinase isoenzyme, albumin, low-density lipoprotein cholesterol, NYHA, serum creatinine, and age were the top seven features of the GNB model. The SHAP force analysis illustrated how created model visualized individualized prediction of CRRT.

Conclusions
Machine learning models were developed to predict CRRT. This contributes to the identification of risk variables for CRRT following CABG surgery in ICU patients and enables the optimization of perioperative managements for patients.
A three-stage approach to identify biomarker signatures for cancer genetic data with survival endpoints
Authors: Wu Xue , Chen Chixiang , Li Zheng , Zhang Lijun , Chinchilli Vernon M. , Wang Ming 
Published: March 2024

AbstractThe identification of prognostic and predictive biomarker signatures is crucial for drug development and providing personalized treatment to cancer patients. However, the discovery process often involves high-dimensional candidate biomarkers, leading to inflated family-wise error rates (FWERs) due to multiple hypothesis testing. This is an understudied area, particularly under the survival framework. To address this issue, we propose a novel three-stage approach for identifying significant biomarker signatures, including prognostic biomarkers (main effects) and predictive biomarkers (biomarker-by-treatment interactions), using Cox proportional hazard regression with high-dimensional covariates. To control the FWER, we adopt an adaptive group LASSO for variable screening and selection. We then derive adjusted p-values through multi-splitting and bootstrapping to overcome invalid p values caused by the penalized approach’s restrictions. Our extensive simulations provide empirical evaluation of the FWER and model selection accuracy, demonstrating that our proposed three-stage approach outperforms existing alternatives. Furthermore, we provide detailed proofs and software implementation in R to support our theoretical contributions. Finally, we apply our method to real data from cancer genetic studies.
Work-related musculoskeletal disorders among dental students: a cross-sectional study integrating the pain adaptation model
Authors: de Almeida Manuel Barbosa , Moleirinho-Alves Paula , Oliveira Raúl 
Published: March 2024

Abstract
Aim
This study aimed to assess the prevalence, anatomical distribution, and contributing biological and psychosocial factors of work-related musculoskeletal disorders (WMSDs) among dental students.

Subject and methods
We used the Nordic Musculoskeletal Questionnaire (NMQ) to determine the prevalence and anatomical distribution of WMSDs, integrating biological and psychosocial variables, including stress, anxiety, and quality of life levels measured by the Perceived Stress Scale (PSS), Generalized Anxiety Disorder Assessment (GAD-7), and World Health Organization Quality of Life-Brief Form (WHOQOL-Bref), respectively. We used chi-square tests to analyze differences between the prevalences of independent groups, and binary logistic regression models to identify potential predictors among sociodemographic and psychosocial characteristics.

Results
A total of 239 students were contacted to participate and 123 answered the questionnaire, setting the response rate at 51.5%. We excluded 18 for being over 30 years old and 25 for having diagnosed and ongoing musculoskeletal injuries, leaving 80 participants. They were 77.5% women and 22.5% men with 22.69 ± 1.63 years, 1.67 ± 0.08 m, 60.44 ± 10.94 kg and a body mass index (BMI) of 21.46 ± 2.61 kg/m2. A total of 91.3% of the participants reported symptoms in at least one body region in the past year. The neck and lower back (63.8%) were the most frequently affected areas, followed by 40% in the shoulders and 31% in the wrists/hands. Over the period of a week, 23.8% experienced symptoms in the lower back, 21.3% in the cervical region, and 15% in both shoulders and wrists/hands. Additionally, male students are 90% more likely to develop WMSDs in the shoulders, and students with anxiety are 49% more prone to refer WMSDs in lower back region.

Conclusion
High WMSDs rates were observed in dental students in neck, low back, and shoulders. Male students and those with increased levels of anxiety were more likely to develop WMSDs. Further investigation focused on the motor-behavior adaptations and psychosocial determinants underpinning these findings is fundamental to fully understand them, allowing the development and implementation of strategies to prevent and manage WMSDs in dental students.
Validation of Korean Version of the Anger Discomfort Scale
Authors: Kim Jisu , Stead Graham B. 
Published: March 2024

AbstractThis article reports on the validation of the Korean version of the Anger Discomfort Scale (ADS-K) in samples comprising college students and community-based adults living in South Korea (n = 765). The Anger Discomfort Scale (ADS) is a 15-item scale designed to assess levels of discomfort associated with both the experience and expression of anger in interpersonal contexts. For psychometric property testing, survey data were analyzed with advanced multivariate methods, focusing on aspects of validity and reliability. The properties of the total scores are examined by descriptive statistics. The results of exploratory factor analysis and confirmatory factor analysis identified the structure of the ADS-K as a well-defined bi-factor model. Correlational analyses indicated that anger discomfort was positively associated with anger suppression, anger expression, trait anxiety, and constructs related to anxiety in interpersonal relationships (e.g., fear of evaluation by others and expectations of negative interpersonal results). Implications for the use of the ADS-K and future directions are suggested.
Multi-start team orienteering problem for UAS mission re-planning with data-efficient deep reinforcement learning
Authors: Lee Dong Ho , Ahn Jaemyung 
Published: March 2024

AbstractIn this paper, we study the Multi-Start Team Orienteering Problem (MSTOP), a mission re-planning problem where vehicles are initially located away from the depot and have different amounts of fuel. We consider/assume the goal of multiple vehicles is to travel to maximize the sum of collected profits under resource (e.g., time, fuel) consumption constraints. Such re-planning problems occur in a wide range of intelligent UAS applications where changes in the mission environment force the operation of multiple vehicles to change from the original plan. To solve this problem with deep reinforcement learning (RL), we develop a policy network with self-attention on each partial tour and encoder-decoder attention between the partial tour and the remaining nodes. We propose a modified REINFORCE algorithm where the greedy rollout baseline is replaced by a local mini-batch baseline based on multiple, possibly non-duplicate sample rollouts. By drawing multiple samples per training instance, we can learn faster and obtain a stable policy gradient estimator with significantly fewer instances. The proposed training algorithm outperforms the conventional greedy rollout baseline, even when combined with the maximum entropy objective. The efficiency of our method is further demonstrated in two classical problems – the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP). The experimental results show that our method enables models to develop more effective heuristics and performs competitively with the state-of-the-art deep reinforcement learning methods.
The Effectiveness of Concept Maps on Students’ Achievement in Science: A Meta-Analysis
Authors: Anastasiou Dimitris , Wirngo Clare Nangsin , Bagos Pantelis 
Published: March 2024

AbstractThis study aims to evaluate the effectiveness of concept maps on science achievement among elementary and secondary education students, including low-achieving students. A systematic search located 55 studies about concept mapping in science achievement published in peer-reviewed journals and dissertations between 1980 and 2020. We extracted 58 independent standardized mean difference effect sizes from 55 eligible studies involving 5,364 students from Grade 3 to Grade 12 who used concept maps for learning in physics/earth science, chemistry, and biology that met the specified design criteria. A random-effects model meta-analysis revealed that the mean effect size was moderate for overall science (g = 0.776). The mean effect sizes varied from moderate to large based on the subject area (g = 0.671 for biology; g = 0.590 for chemistry; g = 1.040 for physics and earth science); these differences between groups were not statistically significant (p = 0.220). Concept maps were generally associated with increased science learning across several learning and teaching conditions, and methodological features (low-achieving students, higher teaching guidance, intermediate grades, low- or middle-income countries, journal publications, and late year of publication). However, we found significant heterogeneity in most subsets. Implications for future research and practice recommendations are discussed.
Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir
Authors: Mpakairi Kudzai S. , Muthivhi Faith F. , Dondofema Farai , Munyai Linton F. , Dalu Tatenda 
Published: March 2024

AbstractEffective water resources management and monitoring are essential amid increasing challenges posed by population growth, industrialization, urbanization, and climate change. Earth observation techniques offer promising opportunities to enhance water resources management and support informed decision-making. This study utilizes Landsat-8 OLI and Sentinel-2 MSI satellite data to estimate chlorophyl-a (chl-a) concentrations in the Nandoni reservoir, Thohoyandou, South Africa. The study estimated chl-a concentrations using random forest models with spectral bands only, spectral indices only (blue difference absorption (BDA), fluorescence line height in the violet region (FLH_violet), and normalized difference chlorophyll index (NDCI)), and combined spectral bands and spectral indices. The results showed that the models using spectral bands from both Landsat-8 OLI and Sentinel-2 MSI performed comparably. The model using Sentinel-2 MSI had a higher accuracy of estimating chl-a when spectral bands alone were used. Sentinel-2 MSI’s additional red-edge spectral bands provided a notable advantage in capturing subtle variations in chl-a concentrations. Lastly, the –chl-a concentration was higher at the edges of the Nandoni reservoir and closer to the reservoir wall. The findings of this study are crucial for improving the management of water reservoirs, enabling proactive decision-making, and supporting sustainable water resource management practices. Ultimately, this research contributes to the broader understanding of the application of earth observation techniques for water resources management, providing valuable information for policymakers and water authorities.
Benchmarking ANI potentials as a rescoring function and screening FDA drugs for SARS-CoV-2 Mpro
Authors: Zengin Irem N. , Koca M. Serdar , Tayfuroglu Omer , Yildiz Muslum , Kocak Abdulkadir 
Published: March 2024

AbstractHere, we introduce the use of ANI-ML potentials as a rescoring function in the host–guest interaction in molecular docking. Our results show that the “docking power” of ANI potentials can compete with the current scoring functions at the same level of computational cost. Benchmarking studies on CASF-2016 dataset showed that ANI is ranked in the top 5 scoring functions among the other 34 tested. In particular, the ANI predicted interaction energies when used in conjunction with GOLD-PLP scoring function can boost the top ranked solution to be the closest to the x-ray structure. Rapid and accurate calculation of interaction energies between ligand and protein also enables screening of millions of drug candidates/docking poses. Using a unique protocol in which docking by GOLD-PLP, rescoring by ANI-ML potentials and extensive MD simulations along with end state free energy methods are combined, we have screened FDA approved drugs against the SARS-CoV-2 main protease (Mpro). The top six drug molecules suggested by the consensus of these free energy methods have already been in clinical trials or proposed as potential drug molecules in previous theoretical and experimental studies, approving the validity and the power of accuracy in our screening method.
Trading-Off Safety with Agility Using Deep Pose Error Estimation and Reinforcement Learning for Perception-Driven UAV Motion Planning
Authors: Kaymaz Mehmetcan , Ayzit Recep , Akgün Onur , Atik Kamil Canberk , Erdem Mustafa , Yalcin Baris , Cetin Gürkan , Ure Nazım Kemal 
Published: March 2024

AbstractNavigation and planning for unmanned aerial vehicles (UAVs) based on visual-inertial sensors has been a popular research area in recent years. However, most visual sensors are prone to high error rates when exposed to disturbances such as excessive brightness and blur, which can lead to catastrophic performance drops in perception and motion planning systems. This study proposes a novel framework to address the coupled perception-planning problem in high-risk environments. This achieved by developing algorithms that can automatically adjust the agility of the UAV maneuvers based on the predicted error rate of the pose estimation system. The fundamental idea behind our work is to demonstrate that highly agile maneuvers become infeasible to execute when visual measurements are noisy. Thus, agility should be traded-off with safety to enable efficient risk management. Our study focuses on navigating a quadcopter through a sequence of gates on an unknown map, and we rely on existing deep learning methods for visual gate-pose estimation. In addition, we develop an architecture for estimating the pose error under high disturbance visual inputs. We use the estimated pose errors to train a reinforcement learning agent to tune the parameters of the motion planning algorithm to safely navigate the environment while minimizing the track completion time. Simulation results demonstrate that our proposed approach yields significantly fewer crashes and higher track completion rates compared to approaches that do not utilize reinforcement learning.