bluuebunny/crossref_metadata_2025_split huggingface dataset:
AI-based X-ray fracture analysis of the distal radius: accuracy between representative classification, detection and segmentation deep learning models for clinical practice
Authors: Russe Maximilian Frederik , Rebmann Philipp , Tran Phuong Hien , Kellner Elias , Reisert Marco , Bamberg Fabian , Kotter Elmar , Kim Suam 
Published: January 2024

ObjectivesTo aid in selecting the optimal artificial intelligence (AI) solution for clinical application, we directly compared performances of selected representative custom-trained or commercial classification, detection and segmentation models for fracture detection on musculoskeletal radiographs of the distal radius by aligning their outputs.Design and settingThis single-centre retrospective study was conducted on a random subset of emergency department radiographs from 2008 to 2018 of the distal radius in Germany.Materials and methodsAn image set was created to be compatible with training and testing classification and segmentation models by annotating examinations for fractures and overlaying fracture masks, if applicable. Representative classification and segmentation models were trained on 80% of the data. After output binarisation, their derived fracture detection performances as well as that of a standard commercially available solution were compared on the remaining X-rays (20%) using mainly accuracy and area under the receiver operating characteristic (AUROC).ResultsA total of 2856 examinations with 712 (24.9%) fractures were included in the analysis. Accuracies reached up to 0.97 for the classification model, 0.94 for the segmentation model and 0.95 for BoneView. Cohen’s kappa was at least 0.80 in pairwise comparisons, while Fleiss’ kappa was 0.83 for all models. Fracture predictions were visualised with all three methods at different levels of detail, ranking from downsampled image region for classification over bounding box for detection to single pixel-level delineation for segmentation.ConclusionsAll three investigated approaches reached high performances for detection of distal radius fractures with simple preprocessing and postprocessing protocols on the custom-trained models. Despite their underlying structural differences, selection of one’s fracture analysis AI tool in the frame of this study reduces to the desired flavour of automation: automated classification, AI-assisted manual fracture reading or minimised false negatives.
Factors influencing primary care access, utilisation and quality of management for patients living with hypertension in West Africa: a scoping review protocol
Authors: Amarteyfio Kezia Naa Amerley Akosua , Bondzie Eugene Paa Kofi , Reichenberger Veronika , Agyepong Irene Akua , Ansah Evelyn K , Diarra Aissa , Mirzoev Tolib , Perel Pablo , Yaogo Maurice , Antwi Edward 
Published: January 2024

IntroductionHypertension, one of the most prevalent non-communicable diseases in West Africa, can be well managed with good primary care. This scoping review will explore what is documented in the literature about factors that influence primary care access, utilisation and quality of management for patients living with hypertension in West Africa.Methods and analysisThe scoping review will employ the approach described by Arksey and O’Malley (2005) . The approach has five stages: (1) formulating the research questions, (2) identifying relevant studies, (3) selecting eligible studies, (4) charting the data and (5) collating, summarising and reporting the results. This review will employ the Preferred Reporting Items for Systematic review and Meta-Analysis extension for scoping reviews to report the results. PubMed, Embase, Scopus, Cairn Info and Google Scholar will be searched for publications from 1 January 2000 to 31 December 2023. Studies reported in English, French or Portuguese will be considered for inclusion. Research articles, systematic reviews, observational studies and reports that include information on the relevant factors that influence primary care management of hypertension in West Africa will be eligible for inclusion. Study participants should be adults (aged 18 years or older). Clinical case series/case reports, short communications, books, grey literature and conference proceedings will be excluded. Papers on gestational hypertension and pre-eclampsia will be excluded.Ethics and disseminationThis review does not require ethics approval. Our dissemination strategy includes peer-reviewed publications, policy briefs, presentations at conferences, dissemination to stakeholders and intervention co-production forums.
Does pain optimisation impact delirium outcomes in critically ill patients? A systematic review and meta-analysis protocol
Authors: Leong Amanda Y. , Burry Lisa , Fiest Kirsten M. , Doig Christopher J. , Niven Daniel J. 
Published: January 2024

BackgroundUntreated pain is associated with short-term and long-term consequences, including post-traumatic stress disorder and insomnia. Side effects of some analgesic medications include dysphoria, hallucinations and delirium. Therefore, both untreated pain and analgesic medications may be risk factors for delirium. Delirium is associated with longer length of stay or cognitive impairment. Our systematic review and meta-analysis will examine the relationship between pain or analgesic medications with delirium occurrence, duration and severity among critically ill adults.Methods and analysisMEDLINE, EMBASE, CINAHL, the Cochrane Central Register of controlled trials and a review of recent conference abstracts will be searched without restriction from inception to 15 May 2023. Study inclusion criteria are: (1) age≥18 years admitted to intensive care; (2) report a measure of pain, analgesic medications and delirium; (3) study design—randomised controlled trial, quasiexperimental designs and observational cohort and case–control studies excluding case reports. Study exclusion criteria are: (1) alcohol withdrawal delirium or delirium tremens; or (2) general anaesthetic emergence delirium; or (3) lab or animal studies. Risk of bias will be assessed with the Risk of Bias V.2 and risk of bias in non-randomised studies tools. There is no language restriction. Occurrence estimates will be transformed using the Freeman-Tukey double arcsine. Point estimates will be pooled using Hartung-Knapp Sidik-Jonkman random effects meta-analysis to estimate a pooled risk ratio. Statistical heterogeneity will be estimated with the I2statistic. Risk of small study effects will be assessed using funnel plots and Egger test. Studies will be analysed for time-varying and unmeasured confounding using E values.Ethics and disseminationEthical approval is not required as this is an analysis of published aggregated data. We will share our findings at conferences and in peer-reviewed journals.PROSPERO registration numberThe finalised protocol was submitted to the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022367715).
Drug driving: a secondary analysis of factors associated with driving under the influence of cannabis in Jamaica
Authors: Lalwani Kunal , Martin Jacqueline , Barton Everard , Frazier Gralyn , Abel Wendel 
Published: January 2024

ObjectivesTo determine cannabis use patterns, the predictive sociodemographic correlates of driving under the influence of cannabis (DUIC) and the association between risk perception and cannabis dependence among vehicle drivers in Jamaica.DesignSecondary data analysis.SettingUsed the Jamaica National Drug Prevalence Survey 2016 dataset.Participants1060 vehicle drivers extracted from the population sample of 4623.Primary and secondary outcome measuresAnalysis used Pearson’s χ2test and logistic regression. ORs and 95% CIs were recorded. A p<0.05 was considered statistically significant.ResultsMore than 10% of Jamaican drivers admitted to DUIC in the past year. Approximately 43.3% of drivers who currently use cannabis reported DUIC only. Evidently, 86.8% of drivers who DUIC were heavy cannabis users. Approximately 30% of drivers with moderate to high-risk perception of smoking cannabis sometimes or often were dependent on cannabis. Notwithstanding, drivers with no to low-risk perception of smoking cannabis sometimes or often were significantly likelier to be dependent (p<0.001 and p<0.001, respectively). Logistic regression highlighted male drivers (OR 4.14, 95% CI 1.59 to 14.20, p=0.009) that were 34 years and under (OR 2.97, 95% CI 1.71 to 5.29, p<0.001) and were the head of the household (OR 2.22, 95% CI 1.10 to 4.75, p=0.031) and operated a machine as part of their job (OR 1.87, 95% CI 1.09 to 3.24, p=0.023) were more likely to DUIC, while those who were married (OR 0.42, 95% CI 0.22 to 0.74, p=0.004) and had achieved a tertiary-level education (OR 0.26, 95% CI 0.06 to 0.76, p=0.031) were less likely.ConclusionsTwo in five Jamaican drivers, who currently smoke cannabis, drive under its influence, with over 85% engaging in heavy use. Public health implications necessitate policy-makers consider mobile roadside drug testing and amending drug-driving laws to meet international standards.
Selective retina therapy with real-time feedback-controlled technology in central serous chorioretinopathy: a 24-month follow-up real-world prospective study
Authors: Yoon Chang Ki , Yu Hyeong Gon 
Published: January 2024

ObjectiveAlthough central serous chorioretinopathy (CSC) treatment using selective retinal therapy (SRT) has presented favourable outcomes, no long-term studies with a real-world clinical practice regimen have been conducted.Methods and analysisWe performed a long-term assessment of CSC treatment using SRT with real-time feedback (RTF) technology. 50 patients (53 eyes) with CSC and more than a 1-month symptom duration were recruited and treated with SRT using a 1.7 µs pulse width, 527 nm neodymium-doped yttrium lithium fluoride (Nd:YLF) laser equipped with an RTF system.ResultsAfter 6 months of treatment, complete subretinal fluid resolution was achieved in 62% of the eyes. The mean best-corrected visual acuity (BCVA; logarithm of the minimum angle of resolution, mean±SD) improved slightly from 0.15±0.18 at baseline to 0.12±0.21 at 6 months (p=0.062). The central retinal thickness (CRT; mean±SD) was reduced significantly from 350.6±100.1 µm at baseline to 268.2±70.6 µm at 6 months (p<0.001). Long-term follow-up revealed significant improvements in BCVA, from 9 months until 24 months, and in CRT, from 1 month until 24 months. No treatment-related adverse events were observed during the 24-month follow-up period.ConclusionOur results suggest that SRT with RTF technology is a long-term safe treatment with anatomical improvement for patients with CSC.
Impact of the minimum wage increase on intimate partner violence (IPV): a quasi-experimental study in South Korea
Authors: Kim Chungah , Bai Yihong , O'Campo Patricia , Chum Antony 
Published: January 2024

BackgroundPoverty is associated with intimate partner violence (IPV), but whether exogenous increases in wage could reduce IPV among low-income women is still unclear. We examined whether the 2018 minimum wage hike led to a reduction in IPV risk among women.MethodsUsing the 2015–2019 Korean Welfare Panel Study, we employed a difference-in-differences (DID) approach to assess the effect of the minimum wage hike on IPV. The analysis focused on married women aged 19 or older. We categorised participants into a target group (likely affected by the minimum wage increase) and a comparison group based on their hourly wage. Three IPV outcomes were examined: verbal abuse, physical threat and physical assault. We conducted DID analyses with two-way fixed-effects models.ResultsThe increase in minimum wage was correlated with a 3.2% decrease in the likelihood of experiencing physical threat among low-income female workers (95% CI: −6.2% to −0.1%). However, the policy change did not significantly influence the risk of verbal abuse, physical assault or a combined IPV outcome. The study also highlights a higher incidence of all IPV outcomes in the target group compared with the comparison group.ConclusionsThe 2018 minimum wage increase in Korea was associated with a modest reduction in physical threat among low-income female workers. While economic empowerment through minimum wage policies may contribute to IPV prevention, additional measures should be explored. Further research is needed to understand the intricate relationship between minimum wage policies and IPV, and evidence-based prevention strategies are crucial to address IPV risk.
How can tracking and tracing systems give us a look at the dark side of the tobacco market?
Authors: Borkowski Filip , Fibbi Edoardo 
Published: January 2024

ObjectiveThe aim of this work is to present possible applications of the systems of tobacco traceability for guiding local enforcement against illicit trade.MethodsThe proposed three-step strategy relies on a robust regression technique and Local Moran’s I, a local indicator of spatial association, and aims at identifying retail outlets with significantly low sales compared to normal market conditions, which can indicate illegal sales activities. The ability of the method to produce alerts pointing to areas subject to illicit trade is tested on synthetic data in terms of precision and accuracy in different scenarios. Other metrics are also provided.ResultsOur approach performs well under different metrics and across various levels of illicit trade prevalence, achieving a precision of 94% under the main scenario and method parametrisation.ConclusionsThe proposed strategy provides high-quality leads for investigations into geographical areas disproportionately susceptible to illicit trade, potentially unveiling any form of illegal sales, including those involving products that have never entered the legal supply chain. Therefore, it can be a valuable tool for law enforcement agencies to tackle illegal sales activities. The findings of this study support also the argument in favour of expanding tobacco traceability systems downstream to the full length of the supply chain.
Methylmercury dietary pathways and bioaccumulation in Arctic benthic invertebrates of the Beaufort Sea
Authors: McClelland Christine , Chételat John , Conlan Kathleen , Aitken Alec , Forbes Mark R. , Majewski Andrew 
Published: January 2024

This study investigated methylmercury (MeHg) concentrations in Arctic benthic invertebrates from two shelf sites in the Canadian Beaufort Sea. Carbon, nitrogen, and sulfur stable isotopes and fatty acids were measured to examine diet influences on MeHg concentrations in 476 individuals from 53 taxa of benthic invertebrates representing three different feeding guilds. Taxonomic identifications were based on DNA-barcoding and traditional taxonomy. MeHg concentrations ranged from 3 to 421 ng/g dry weight and increased over three trophic levels (δ15N range = 4.4–14.2‰). Organic matter sources had small but significant influences on MeHg bioaccumulation in the benthic food web. Carbon stable isotope ratios (δ13C, range = −25.5 to −19.8‰) were positively correlated with MeHg concentrations, suggesting greater reliance on benthic carbon contributed to higher concentrations. Sulfur stable isotopes were unrelated to MeHg concentrations. Fatty acids suggested feeding on diatoms versus dinoflagellates, and reliance on benthic resources influenced MeHg concentrations. Higher MeHg concentrations were observed at the site closer to the Mackenzie River mouth than the Cape Bathurst site. This study generated the most taxonomically rich dataset of MeHg concentrations in invertebrates from the Arctic marine benthos to date and provides a basis for future research on food web MeHg dynamics in the Canadian Beaufort Sea.
Existence of gradient CKV and gradient conformally stationary LRS spacetimes
Authors: Koh Seoktae , Sherif A. M. , Tumurtushaa G. 
Published: January 2024

AbstractIn this work, we study the existence of gradient (proper) CKVs in locally rotationally symmetric spacetimes (LRS), those CKVs in the space spanned by the tangent to observers’ congruence and the preferred spatial direction, allowing us to provide a (partial) characterization of gradient conformally static (GCSt) LRS solutions. Irrrotational solutions with non-zero spatial twist admit an irrotational timelike gradient conformal Killing vector field and hence are GCSt. In the case that both the vorticity and twist vanish, that is, restricting to the LRS II subclass, we obtain the necessary and sufficient condition for the spacetime to admit a gradient CKV. This is given by a single wave-like PDE, whose solutions are in bijection to the gradient CKVs on the spacetime. We then introduce a characterization of these spacetimes as GCSt using the character of the divergence of the CKV, provided that the metric functions of the spacetimes obey certain inequalities.
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression
Authors: Zhou Lijia , Koehler Frederic , Sutherland Danica J. , Srebro Nathan 
Published: January 2024

We study a localized notion of uniform convergence known as an “optimistic rate” [
34
,
39
] for linear regression with Gaussian data. Our refined analysis avoids the hidden constant and logarithmic factor in existing results, which are known to be crucial in high-dimensional settings, especially for understanding interpolation learning. As a special case, our analysis recovers the guarantee from Koehler et al. [
21
], which tightly characterizes the population risk of low-norm interpolators under the benign overfitting conditions. Our optimistic rate bound, though, also analyzes predictors with arbitrary training error. This allows us to recover some classical statistical guarantees for ridge and LASSO regression under random designs and helps us obtain a precise understanding of the excess risk of near-interpolators in the over-parameterized regime.
RANGO: A Novel Deep Learning Approach to Detect Drones Disguising from Video Surveillance Systems
Authors: Han Jin , Ren Yun-Feng , Brighente Alessandro , Conti Mauro 
Published: January 2024

Video surveillance systems provide means to detect the presence of potentially malicious drones in the surroundings of critical infrastructures. In particular, these systems collect images and feed them to a deep-learning classifier able to detect the presence of a drone in the input image. However, current classifiers are not efficient in identifying drones that disguise themselves with the image background, e.g., hiding in front of a tree. Furthermore, video-based detection systems heavily rely on the image’s brightness, where darkness imposes significant challenges in detecting drones. Both these phenomena increase the possibilities for attackers to get close to critical infrastructures without being spotted and hence be able to gather sensitive information or cause physical damages, possibly leading to safety threats.
In this article, we propose RANGO, a drone detection arithmetic able to detect drones in challenging images where the target is difficult to distinguish from the background. RANGO is based on a deep learning architecture that exploits a Preconditioning Operation (PREP) that highlights the target by the difference between the target gradient and the background gradient. The idea is to highlight features that will be useful for classification. After PREP, RANGO uses multiple convolution kernels to make the final decision on the presence of the drone. We test RANGO on a drone image dataset composed of multiple already-existing datasets to which we add samples of birds and planes. We then compare RANGO with multiple currently existing approaches to show its superiority. When tested on images with disguising drones, RANGO attains an increase of 6.6% mean Average Precision (mAP) compared to YOLOv5 solution. When tested on the conventional dataset, RANGO improves the mAP by approximately 2.2%, thus confirming its effectiveness also in the general scenario.
Steady-state Visual Evoked Potentials Reveal Dynamic (Re)allocation of Spatial Attention during Maintenance and Utilization of Visual Working Memory
Authors: Chota Samson , Bruat Arnaud T. , Van der Stigchel Stefan , Strauch Christoph 
Published: January 2024

Abstract
Visual working memory (VWM) allows storing goal-relevant information to guide future behavior. Prior work suggests that VWM is spatially organized and relies on spatial attention directed toward locations at which memory items were encoded, even if location is task-irrelevant. Importantly, attention often needs to be dynamically redistributed between locations, for example, in preparation for an upcoming probe. Very little is known about how attentional resources are distributed between multiple locations during a VWM task and even less about the dynamic changes governing such attentional shifts over time. This is largely due to the inability to use behavioral outcomes to reveal fast dynamic changes within trials. We here demonstrated that EEG steady-state visual evoked potentials (SSVEPs) successfully track the dynamic allocation of spatial attention during a VWM task. Participants were presented with to-be-memorized gratings and distractors at two distinct locations, tagged with flickering discs. This allowed us to dynamically track attention allocated to memory and distractor items via their coupling with space by quantifying the amplitude and coherence of SSVEP responses in the EEG signal to flickering stimuli at the former memory and distractor locations. SSVEP responses did not differ between memory and distractor locations during early maintenance. However, shortly before probe comparison, we observed a decrease in SSVEP coherence over distractor locations indicative of a reallocation of spatial attentional resources. RTs were shorter when preceded by stronger decreases in SSVEP coherence at distractor locations, likely reflecting attentional shifts from the distractor to the probe or memory location. We demonstrate that SSVEPs can inform about dynamic processes in VWM, even if location does not have to be reported by participants. This finding not only supports the notion of a spatially organized VWM but also reveals that SSVEPs betray a dynamic prioritization process of working memory items and locations over time that is directly predictive of memory performance.
Encoding-related Brain Activity Predicts Subsequent Trial-level Control of Proactive Interference in Working Memory
Authors: Samrani George , Persson Jonas 
Published: January 2024

Abstract
Proactive interference (PI) appears when familiar information interferes with newly acquired information and is a major cause of forgetting in working memory. It has been proposed that encoding of item–context associations might help mitigate familiarity-based PI. Here, we investigate whether encoding-related brain activation could predict subsequent level of PI at retrieval using trial-specific parametric modulation. Participants were scanned with event-related fMRI while performing a 2-back working memory task with embedded 3-back lures designed to induce PI. We found that the ability to control interference in working memory was modulated by level of activation in the left inferior frontal gyrus, left hippocampus, and bilateral caudate nucleus during encoding. These results provide insight to the processes underlying control of PI in working memory and suggest that encoding of temporal context details support subsequent interference control.