bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records
Authors: Ferdinands Gerbrich , Schram Raoul , de Bruin Jonathan , Bagheri Ayoub , Oberski Daniel L. , Tummers Lars , Teijema Jelle Jasper , van de Schoot Rens 
Published: June 2023

Abstract
Background
Conducting a systematic review demands a significant amount of effort in screening titles and abstracts. To accelerate this process, various tools that utilize active learning have been proposed. These tools allow the reviewer to interact with machine learning software to identify relevant publications as early as possible. The goal of this study is to gain a comprehensive understanding of active learning models for reducing the workload in systematic reviews through a simulation study.

Methods
The simulation study mimics the process of a human reviewer screening records while interacting with an active learning model. Different active learning models were compared based on four classification techniques (naive Bayes, logistic regression, support vector machines, and random forest) and two feature extraction strategies (TF-IDF and doc2vec). The performance of the models was compared for six systematic review datasets from different research areas. The evaluation of the models was based on the Work Saved over Sampling (WSS) and recall. Additionally, this study introduces two new statistics, Time to Discovery (TD) and Average Time to Discovery (ATD).

Results
The models reduce the number of publications needed to screen by 91.7 to 63.9% while still finding 95% of all relevant records (WSS@95). Recall of the models was defined as the proportion of relevant records found after screening 10% of of all records and ranges from 53.6 to 99.8%. The ATD values range from 1.4% till 11.7%, which indicate the average proportion of labeling decisions the researcher needs to make to detect a relevant record. The ATD values display a similar ranking across the simulations as the recall and WSS values.

Conclusions
Active learning models for screening prioritization demonstrate significant potential for reducing the workload in systematic reviews. The Naive Bayes + TF-IDF model yielded the best results overall. The Average Time to Discovery (ATD) measures performance of active learning models throughout the entire screening process without the need for an arbitrary cut-off point. This makes the ATD a promising metric for comparing the performance of different models across different datasets.
Reviewing the current state of virtual reality integration in medical education – a scoping review protocol
Authors: Mergen Marvin , Meyerheim Marcel , Graf Norbert 
Published: June 2023

Abstract
Background
Due to an increasing focus of medical curricula on clinical decision-making skills, new learning tools are constantly developed. Virtual reality (VR) is one of the emerging technologies with the potential to improve health professionals’ education. Highly realistic learning experiences with repeatable training scenarios can be created within a protected environment that is independent from real patients' presence. Our project “medical tr.AI.ning” is following this approach aiming to simulate immersive virtual first-person scenarios with intelligent, interactable virtual patients. So far, VR has been mainly used in surgical training, but there is evidence for effectiveness in training different procedural skills, such as cardiopulmonary resuscitation, knowledge acquisition, and improvement of reasoning and creativity, while still being cost-effective.
The objective of this scoping review is to explore the usage and identify key areas of VR applications in the field of medical education. Furthermore, the corresponding requirements, evaluation methods and outcomes, advantages, and disadvantages will be covered.

Methods
This scoping review protocol implements the updated JBI Scoping Review Methodology. In March 2022, a preliminary literature research in PubMed was performed by two independent reviewers to refine search terms and strategy as well as inclusion criteria of the protocol, accounting for actuality and scientific relevance. The final search will be conducted using PubMed, ScienceDirect, Cochrane Library, Web of Science Core Collection, and JBI Evidence Synthesis. Search, study screening, and data extraction will be done in parallel and independently by two reviewers. Discrepancies will be handled by consensus or consulting a third review author.

Discussion
With this scoping review, we anticipate collating the range of application of VR in medical education while using a transparent and reproducible search strategy. This may contribute to the design and development of novel educational VR platforms and their integration into medical curricula while pointing out previous omissions and pitfalls.
Spin on adverse effects in abstracts of systematic reviews of orthodontic interventions: a cross-sectional study (part 2)
Authors: Steegmans Pauline A. J. , Di Girolamo Nicola , Meursinge Reynders Reint A. 
Published: June 2023

Abstract
Background
It is critical that abstracts of systematic reviews transparently report both the beneficial and adverse effects of interventions without misleading the readers. This cross-sectional study assessed whether adverse effects of interventions were reported or considered in abstracts of systematic reviews of orthodontic interventions and whether spin on adverse effects was identified when comparing the abstracts with what was sought and reported in these reviews.

Methods
This cross-sectional study (part 2 of 2) used the same sample of 98 systematic reviews orthodontic interventions as used in part 1. Eligible reviews were retrieved from the Cochrane Database of Systematic Reviews and the 5 leading orthodontic journals between August 1 2009 and July 31 2021. Prevalence proportions were sought for 3 outcomes as defined in the published protocol. Univariable logistic regression models were built to explore associations between the presence of spin in the abstract and a series of predictors. Odds ratios (OR) 95% confidence intervals (95% CI) were used to quantify the strength of associations and their precision.

Results
76.5% (75/98) of eligible reviews reported or considered (i.e., discussed, weighted etc.) potential adverse effects of orthodontic interventions in the abstract and the proportion of spin on adverse effects was 40.8% (40/98) in the abstract of these reviews. Misleading reporting was the predominant category of spin, i.e., 90% (36/40). Our explorative analyses found that compared to the Cochrane Database of Systematic Reviews all 5 orthodontic journals had similar odds of the presence of spin on adverse effects in abstracts of systematic reviews of orthodontic interventions. The odds of the presence of spin did not change over the sampled years (OR: 1.03, 95% CI: 0.9 to 1.16) and did not depend on the number of authors (OR: 0.93, 95% CI: 0.71 to 1.21), or on the type of orthodontic intervention (OR: 1.1, 95% CI: 0.45 to 2.67), or whether conflicts of interests were reported (OR: 0.74, 95% CI: 0.32 to 1.68).

Conclusion
End users of systematic reviews of orthodontic interventions have to be careful when interpreting results on adverse effects in the abstracts of these reviews, because they could be jeopardized by uncertainties such as not being reported and misleading reporting as a result of spin.
Ancient mural inpainting via structure information guided two-branch model
Authors: Deng Xiaochao , Yu Ying 
Published: June 2023

Abstract
Ancient murals are important cultural heritages for our exploration of ancient civilizations and are of great research value. Due to long-time exposure to the environment, ancient murals often suffer from damage (deterioration) such as cracks, scratches, corrosion, paint loss, and even large-region falling off. It is an urgent work to protect and restore these damaged ancient murals. Mural inpainting techniques refer to virtually filling the deteriorated regions by reconstructing the structure and texture elements of the mural images. Most existing mural inpainting approaches fail to fill loss contents that contain complex structures and diverse patterns since they neglect the importance of structure guidance. In this paper, we propose a structure-guided two-branch model based on the generative adversarial network (GAN) for ancient mural inpainting. In the proposed model, the mural inpainting process can be divided into two stages: structure reconstruction and content restoration. These two stages are conducted by using a structure reconstruction network (SRN) and a content restoration network (CRN), respectively. In the structure reconstruction stage, SRN employs the Gated Convolution and the Fast Fourier Convolution (FFC) residual block to reconstruct the missing structures of the damaged murals. In the content restoration stage, CRN uses the structures (generated by SRN) to guide the missing content restoration of the murals. We design a two-branch parallel encoder to improve the texture and color restoration quality for the missing regions of the murals. Moreover, we propose a cascade attention module that can capture long-term relevance information in the deep features. It helps to alleviate the texture-blur and color-bias problem. We conduct experiments on both simulated and real damaged murals, and compare our inpainting results with other four competitive approaches. Experimental results show that our proposed model outperforms other approaches in terms of texture clarity, color consistency and structural continuity of the restored mural images. In addition, the mural inpainting results of our model can achieve comparatively high quantitative evaluation metrics.
Constraints on magma storage conditions based on geodetic volume change and erupted magma volume and application to the 2011 and 2018 eruptions at Kirishima Shinmoe-dake volcano, Japan
Authors: Kozono Tomofumi , Koyaguchi Takehiro , Ueda Hideki , Ozawa Taku , Yamasaki Tadashi 
Published: June 2023

Abstract
We investigated magma storage conditions prior to the 2011 and 2018 eruptions at Kirishima Shinmoe-dake volcano in Japan based on the relationship between geodetic volume change of magma chamber and erupted magma volume. We derived an analytical expression for the ratio of the erupted magma volume to the geodetic volume change (“volume ratio”), which was formulated as a function of parameters related to the magma storage conditions. This expression shows that the volume ratio is strongly dependent on the effective compressibility of the magma chamber, which in turn depends on the rigidity of surrounding host rocks and shape of the chamber. For the Shinmoe-dake eruptions, the magnitude of the volume change (i.e., deflation) of a spherical magma chamber associated with lava effusion was estimated based on geodetic observations. The erupted magma volume was estimated from a SAR image analysis of the lava accumulation inside the summit crater. Based on these observations, we estimated that the volume ratio in 2011 and 2018 was 2.69 and 2.33, respectively. Substituting the estimated volume ratio into the analytical expression revealed that the observed geodetic data and volume ratio can be explained only when the magma chamber, which was assumed to be spherical, is filled with bubble-free magma. This result suggests that efficient gas segregation from the chamber occurred prior to the eruptions. Our results indicate that combining multi-observation data based on the volume ratio provides valuable information about the magma storage process, such as the behavior of the gas phase in the magma chamber.

Graphical Abstract
The ethical implications of resonance theory
Authors: Taylor Charles 
Published: June 2023

AbstractIn the first part of this paper, I want to look at the ethical implications of Hartmut Rosa’s Resonance theory for a critical theory of society. I know that this widening of the scope of critical theory is an important objective which Hartmut has pursued. Then I will look at some of the sources of resonance theory in the poetry of the Romantic period. These still provide the basis for important Resonanzachsen today. At the end of this essay, I deal with the issue of the epistemic status of the convictions this poetry inspires.
Diagnostic accuracy of shear wave elastography in evaluating renal fibrosis in children with chronic kidney disease: a comparative study with nuclear scan
Authors: Yadav Abhishek Kumar , Sherwani Poonam , Yhoshu Enono , Dhingra Vandana Kumar , Bhat Nowneet Kumar 
Published: June 2023

Abstract
Background
Chronic kidney disease (CKD) is a significant health issue in pediatric patients due to fibrosis progression. Shear wave elastography (SWE) is a noninvasive technique used to assess fibrosis in CKD, but its efficacy needs to be better established. This study aimed to compare SWE with nuclear scan in assessing fibrosis in pediatric CKD patients.

Aim
To determine the area of scarring/fibrosis of each kidney using shear wave elastography in chronic kidney disease and compare it with technetium-99m dimercaptosuccinic acid (DMSA) results.

Methods
A prospective study included 39 chronic kidney disease patients who underwent shear wave elastography and grayscale ultrasound. DMSA scans were performed to identify scar areas of the kidneys. Young modulus was recorded for each pole of both kidneys and compared with scar areas on DMSA. Thirty-nine age-matched controls underwent shear wave elastography to estimate the average elasticity value in the normal population.

Results
Thirty-nine CKD patients underwent this study, with 10 females and 29 males. The median age was 6.5 years. The cutoff value of cortical thickness ≤ 10.2 predicted scar on DMSA with a sensitivity of 79% and a specificity of 77%. The cutoff value of elasticity value ≥ 5.57 kPa predicted scar on DMSA with a sensitivity of 87% and a specificity of 96%. Median SWE values were significantly higher with the scar on DMSA (12.6 kPa) compared to no scar on DMSA (4.1 kPa). The controls mean values and standard deviation were 2.42 kPa and 0.45 kPa, respectively.

Conclusions
Shear wave elastography has revealed that patients with chronic kidney disease exhibit higher values in the areas where scarring has occurred, compared to non-scarred areas. Fortunately, integrating shear wave elastography into routine ultrasonography assessments is a straightforward and painless process that requires no additional preparation from the patient. Not only is this method time efficient, but it also eliminates the need for potentially risky radiation exposure from radionuclide tests in the future.
The Emergency nurse Protocols Initiating Care—Sydney Triage to Admission Risk Tool (EPIC-START) trial: protocol for a stepped wedge implementation trial
Authors: Curtis Kate , Dinh Michael M. , Shetty Amith , Kourouche Sarah , Fry Margaret , Considine Julie , Li Ling , Lung Thomas , Shaw Timothy , Lam Mary K. , Murphy Margaret , Alkhouri Hatem , Aggar Christina , Russell Saartje Berendsen , Seimon Radhika V. , Hughes James A. , Varndell Wayne , Shaban Ramon Z. 
Published: June 2023

Abstract
Introduction
Emergency department (ED) overcrowding is a global problem and a threat to the quality and safety of emergency care. Providing timely and safe emergency care therein is challenging. To address this in New South Wales (NSW), Australia, the Emergency nurse Protocol Initiating Care—Sydney Triage to Admission Risk Tool (EPIC-START) was developed. EPIC-START is a model of care incorporating EPIC protocols, the START patient admission prediction tool, and a clinical deterioration tool to support ED flow, timely care, and patient safety. The aim of this study is to evaluate the impact of EPIC-START implementation across 30 EDs on patient, implementation, and health service outcomes.

Methods and analysis
This study protocol adopts an effectiveness-implementation hybrid design (Med Care 50: 217-226, 2012) and uses a stepped–wedge cluster randomised control trial of EPIC-START, including uptake and sustainability, within 30 EDs across four NSW local health districts spanning rural, regional, and metropolitan settings. Each cluster will be randomised independently of the research team to 1 of 4 dates until all EDs have been exposed to the intervention. Quantitative and qualitative evaluations will be conducted on data from medical records and routinely collected data, and patient, nursing, and medical staff pre- and post-surveys.

Ethics and dissemination
Ethical approval for the research was received from the Sydney Local Health District Research Ethics Committee (Reference Number 2022/ETH01940) on 14 December 2022.

Trial registration
Australian and New Zealand Clinical trial, ACTRN12622001480774p. Registered on 27 October 2022.
Quick clay monitoring using distributed acoustic sensing: A case study from Rissa, Norway
Authors: Rørstadbotnen Robin André , Dong Hefeng , Landrø Martin , Duffaut Kenneth , Growe Kevin , Kakhkhorov Umedzhon , Wienecke Susann , Jacobsen Joacim 
Published: June 2023

Quick clay avalanche is one of the most devastating landslide types worldwide. Hence, an early warning system is in demand to mitigate the fatal consequences caused by such events. To address this, distributed acoustic sensing data are collected in an area containing quick clay deposits between July 2021 and February 2022 in Rissa, Norway, while a new road is constructed on the quick clay. Road construction can induce unwanted changes to the mass balance in the clay, and previous landslides have been triggered by such changes. For this purpose, passive and active data are collected to test and compare various analysis methods. Using extracted Rayleigh wave dispersion from active sledgehammer shots, shear-wave velocity depth profiles covering the first 15 m could be estimated and compared using a linearized and a nonlinear surface wave inversion method. Furthermore, ambient noise crosscorrelation is used to obtain the dispersion from the ambient noise and associated shear-wave velocity profiles, providing two possible data collection methods for the early warning system. The obtained dispersion curves and the estimated shear-wave velocity profiles show small time-lapse variation during the acquisition period (up to approximately 23 m/s), where the variation is within one standard deviation. Such a small variation suggests that the construction work and the extra load added to the quick clay do not alter the quick clay’s properties. Nevertheless, the obtained results capture the nonrepeatability effects within the acquisition period and provide reference curves for the study area at undisturbed conditions and valuable information for future comparisons to refer to potential failure scenarios. This is the first step in exploring an early warning system for quick clay landslides using fiber-optic cables. Further work will investigate the possibility of automatizing the system and improving the accuracy of the sensing system.
3D DC inversion, visualization, and processing of dense time-lapse data in fine domains applied to remediation monitoring
Authors: Domenzain Diego , Liu Lichao , Yélamos Vela Iván , Christiansen Anders Vest 
Published: June 2023

Using electrical direct-current (DC) data to monitor the subsurface is an efficient solution for observing changes in the shallow subsurface. Recent advances in instrumentation enable the acquisition of large data sets over 3D domains in a reasonable time. We have developed an inversion approach capable of handling very large amounts of data on a finely discretized domain, which enables the delivery of time-lapse results of dense data acquisitions in a feasible time and with high resolution. To spatially represent 3D DC data, a visualization method is introduced that formally generalizes the focusing point of a common pseudosection in three dimensions. The method physically describes subsurface properties for simple geologic scenarios. In addition, the focusing points are used to design our 3D measuring protocol. We develop a DC data processing scheme using the full time-domain induced polarization (IP) waveform data by applying two novel physics-based criteria that enforce charge buildup and steady state. These criteria are tested on DCIP field data and find that 98% of the filtered DC data have less than 1% relative error. The 3D visualization method and inversion algorithm are evaluated on synthetic scenarios of 3D DC dense borehole data. Then, we indicate time-lapse results of DC field data acquired at an uncontaminated site where a remediation agent was injected at depth. The data are acquired with a new DCIP instrument capable of measuring dense data sets at fast acquisition times. Accounting for DCIP data acquisition, signal processing, and 3D inversion of 60,000 DC measurements, our results locate in 14 h the 3D spread of the remediation agent in a [Formula: see text] volume.
User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications
Authors: Fritz Bradley A. , Pugazenthi Sangami , Budelier Thaddeus P. , Tellor Pennington Bethany R. , King Christopher R. , Avidan Michael S. , Abraham Joanna 
Published: June 2023

BACKGROUND:
Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians.

METHODS:
Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis.

RESULTS:
During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5).

CONCLUSIONS:
Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.