bluuebunny/crossref_metadata_2025_split huggingface dataset:
Population Segmentation for COVID-19 Vaccine Outreach: A Clustering Analysis and Implementation in Missouri
Authors: Chessen Eleanor G. , Ganser Madelyn E. , Paulish Colin A. , Malik Aamia , Wishner Allison G. , Turabelidze George , Glenn Jeffrey J. 
Published: April 2023

Objectives:
The purpose of this work was to segment the Missouri population into unique groups related to COVID-19 vaccine acceptance using data science and behavioral science methods to develop tailored vaccine outreach strategies.

Methods:
Cluster analysis techniques were applied to a large data set that aggregated vaccination data with behavioral and demographic data from the American Community Survey and Deloitte's HealthPrism™ data set. Outreach recommendations were developed for each cluster, specific to each group's practical and motivational barriers to vaccination.

Results:
Following selection procedures, 10 clusters—or segments—of census tracts across Missouri were identified on the basis of k-means clustering analysis of 18 different variables. Each cluster exhibited unique geographic, demographic, socioeconomic, and behavioral patterns, and outreach strategies were developed on the basis of each cluster's practical and motivational barriers.

Discussion:
The segmentation analysis served as the foundation for “working groups” comprising the 115 local public health agencies (LPHAs) across the state. LPHAs with similar community segments in their service area were grouped together to discuss their communities' specific challenges, share lessons learned, and brainstorm new approaches. The working groups provided a novel way for public health to organize and collaborate across the state. Widening the aperture beyond Missouri, population segmentation via cluster analysis is a promising approach for public health practitioners interested in developing a richer understanding of the types of populations they serve. By pairing segmentation with behavioral science, practitioners can develop outreach programs and communications campaigns that are personalized to the specific behavioral barriers and needs of the population in focus. While our work focused on COVID-19, this approach has broad applicability to enhance the way public health practitioners understand the populations they serve to deliver more tailored services.
BactoBattle: A game-based learning companion for medical bacteriology
Authors: Imwattana Korakrit , Methawirune Areeya , Voralakkulchai Isara , Ngamskulrungroj Popchai 
Published: April 2023

A card game called “BactoBattle” was developed to help medical students who have just started learning medical bacteriology improve their learning efficacy and learning satisfaction, especially on the topic of antimicrobial resistance. Copies of the game were placed in the students’ study room (approximately 1 set per 12 students) and made available to the students throughout the study period so that students could choose to play the game during their free time if desired. After the study period had ended, the students were asked to complete a questionnaire and a posttest. In total, 33 students completed the questionnaire, and were split into two groups: the player group, comprising 12 (36.4%) students who had played the game, and the non-player group. The player group perceived that they could memorize more knowledge compared to the non-player group and indeed recorded higher posttest scores than the non-player group (10.4 vs. 8.3, p = 0.031). However, there was no difference in the learning motivation (p = 0.441) nor enjoyment (p = 0.562) between the two groups. A majority of the players said they would continue playing the game after the study period and a majority of both the players and non-players would recommend the game to other students. In short, the BactoBattle game can be a useful tool to improve the learning efficacy of students, but its effect on learning satisfaction remains unclear.
Thyroid cancer polygenic risk score combined with deep learning analysis of ultrasound images improves the classification of thyroid nodules as benign or malignant
Authors: Pozdeyev Nikita , Dighe Manjiri , Barrio Martin , Raeburn Christopher , Smith Harry , Fisher Matthew , Chavan Sameer , Rafaels Nicholas , Shortt Jonathan A. , Lin Meng , Leu Michael G. , Clark Toshimasa , Marshall Carrie , Haugen Bryan R. , Subramanian Devika , Center Regeneron Genetics , Crooks Kristy , Gignoux Christopher , Cohen Trevor 
Published: April 2023

AbstractEvaluating thyroid nodules to rule out malignancy is a very common clinical task. Image-based clinical and machine learning risk stratification schemas rely on the presence of thyroid nodule high-risk sonographic features. However, this approach is less suitable for diagnosing malignant thyroid nodules with a benign appearance on ultrasound. In this study, we developed thyroid cancer polygenic risk scoring (PRS) to complement deep learning analysis of ultrasound images. When the output of the deep learning model was combined with thyroid cancer PRS and genetic ancestry estimates, the area under the receiver operating characteristic curve (AUROC) of the benign vs. malignant thyroid nodule classifier increased from 0.83 to 0.89 (DeLong, p-value = 0.007). The combined deep learning and genetic classifier achieved a clinically relevant sensitivity of 0.95, 95 CI [0.88-0.99], specificity of 0.63 [0.55-0.70], and positive and negative predictive values of 0.47 [0.41-0.58] and 0.97 [0.92-0.99], respectively. An improved AUROC was consistent in ancestry-stratified analysis in Europeans (0.83 and 0.87 for deep-learning and deep learning combined with PRS classifiers, respectively). An elevated PRS was associated with a greater risk of thyroid cancer structural disease recurrence (ordinal logistic regression, p-value = 0.002). This study demonstrates that augmenting ultrasound image analysis with PRS improves diagnostic accuracy, paving the way for developing the next generation of clinical risk stratification algorithms incorporating inherited risk for developing thyroid malignancy.
Validation of automated hippocampus volume assessment using deep learning convolutional neural networks in patients with Alzheimer’s disease
Authors: Nabila Ibrahim Nur Shahidatul , Suppiah Subapriya , Ibrahim Buhari , Mohad Azmi Nur Hafizah , Seriramulu Vengkatha Priya , Mohamad Mazlyfarina , Hanafi Marsyita , Sallehuddin Hakimah Mohammad , Razali Rizah Mazzuin , Harrun Noor Harzana 
Published: April 2023

AbstractBackgroundDementia is a spectrum of diseases characterised by a progressive and irreversible decline in cognitive function. Appropriate tools and references are essential for evaluating individuals’ cognitive levels, especially hippocampal volume as it is the commonly used biomarker in detecting Alzheimer’s disease (AD). It is important to note that while there is no cure for dementia, early intervention and support can greatly improve the lives of those affected.MethodOngoing research is being conducted to develop new treatments and improve our understanding of the disease by using VBM to compare sensitivity and specificity with the HippoDeep toolbox.ResultWe were able to validate AD’s hippocampal volume compared to age-matched healthy controls (HC) based on HippoDeep Model by comparing it with VBM as the reference standard.ConclusionThere are significant differences between hippocampal volume in AD and HC that have been detected using VBM and HippoDeep analysis.
Sex-specific developmental scales improve early childhood developmental surveillance
Authors: Sudry Tamar , Amit Guy , Zimmerman Deena R , Tsadok Meytal Avgil , Baruch Ravit , Yardeni Hadar , Akiva Pinchas , Moshe Dror Ben , Bachmat Eitan , Sadaka Yair 
Published: April 2023

AbstractIntroductionRoutine developmental surveillance is fundamental for timely identification of developmental delays. We explored sex-related differences in milestone attainment rate and evaluated the clinical need for sex-specific developmental scales.MethodsThis is a retrospective cross-sectional study, utilizing data from a national child surveillance program. The study included children from birth to six years of age, assessed between 2014-2021 (n=643,958 and n=309,181 for the main and validation cohorts, respectively).We measured the differences between sexes in normative attainment age of 59 milestones from four developmental domains and calculated the projected error rates when conducting unified vs. sex-specific surveillance.ResultsGirls preceded boys in most milestones of all domains. Conducting developmental surveillance using unified rather than sex-specific scales resulted in potential missing of girls at risk of developmental delay (19.3% of failed assessments), and false alerts for boys (5.9%).ConclusionThese findings suggest that using sex-specific scales may improve the accuracy of early childhood developmental surveillance.
Performance of ChatGPT on Clinical Medicine Entrance Examination for Chinese Postgraduate in Chinese
Authors: Liu Xiao , Fang Changchang , Yan Ziwei , Liu Xiaoling , Jiang Yuan , Cao Zhengyu , Wu Maoxiong , Chen Zhiteng , Ma Jianyong , Yu Peng , Zhu Wengen , Abudukeremu Ayiguli , Wang Yue , Chen Yangxin , Zhang Yuling , Wang Jingfeng 
Published: April 2023

AbstractBackgroundThe ChatGPT, a Large-scale language models-based Artificial intelligence (AI), has fueled interest in medical care. However, the ability of AI to understand and generate text is constrained by the quality and quantity of training data available for that language. This study aims to provide qualitative feedback on ChatGPT’s problem-solving capabilities in medical education and clinical decisionmaking in Chinese.MethodsA dataset of Clinical Medicine Entrance Examination for Chinese Postgraduate was used to assess the effectiveness of ChatGPT3.5 in medical knowledge in Chinese language. The indictor of accuracy, concordance (explaining affirms the answer) and frequency of insights was used to assess performance of ChatGPT in original and encoding medical questions.ResultAccording to our evaluation, ChatGPT received a score of 153.5/300 for original questions in Chinese, which is slightly above the passing threshold of 129/300. Additionally, ChatGPT showed low accuracy in answering open-ended medical questions, with total accuracy of 31.5%. While ChatGPT demonstrated a commendable level of concordance (achieving 90% concordance across all questions) and generated innovative insights for most problems (at least one significant insight for 80% of all questions).ConclusionChatGPT’s performance was suboptimal for medical education and clinical decision-making in Chinese compared with in English. However, ChatGPT demonstrated high internal concordance and generated multiple insights in Chinese language. Further research should investigate language-based differences in ChatGPT’s healthcare performance.
Caught in the data quality trap: A case study from the evaluation of a new digital technology supporting routine health data collection in Southern Tanzania
Authors: Unkels Regine , Ahmad Aziz , Manzi Fatuma , Kasembe Asha , Kapologwe Ntuli A. , Nabiev Rustam , Berndtsson Maria , Hirose Atsumi , Hanson Claudia 
Published: April 2023

AbstractBackgroundHealth service data from Health Management Information Systems is important for decision-making at all health system levels. Data quality issues in low-and-middle-income countries hamper data use however.Smart Paper Technology, a novel digital-hybrid technology, was designed to overcome quality challenges through automated digitization. Here we assessed the impact of the novel system on data quality dimensions, metrics and indicators as proposed by the World Health Organization’sData Quality Review Toolkit.MethodsThis cross-sectional study was conducted between November 2019 and October 2020 in 13 health facilities sampled from 33 facilities of one district in rural Tanzania, where we implementedSmart Paper Technology. We assessed the technology’s data quality for maternal health care against the standardDistrict Health Information System-2applied in Tanzania.ResultsSmart Paper Technologyperformed slightly better than theDistrict Health Information System-2regardingconsistency between related indicatorsandoutliers. We found <10% difference between related indicators for 62% of the facilities for the new system versus 38% for the standard system in the reference year.Smart Paper Technologywas inferior toDistrict Health Information System-2data in terms ofcompleteness. We observed that data on 1stantenatal care visitswere complete ⍰ 90% in only 76% of facilities for the new system against 92% for the standard system. For the indicatorinternal consistency over time73%, 59% and 45% of client numbers for antenatal, labour and postnatal care recorded in the standard system were documented in the new system.Smart Paper Technologyforms were submitted in 83% of the months for all service areas.ConclusionOur results suggest that not all client encounters were documented inSmart Paper Technology, affecting data completeness and partly consistency. The novel system was unable to leverage opportunities from automated processes because primary documentation was poor. Low buy-in of policymakers and lack of internal quality assurance may have affected data quality of the new system. We emphasize the importance of including policymakers in evaluation planning to co-design a data quality monitoring system and to agree on a realistic way to ensure reporting of routine health data to national level.
Relating mutational signature exposures to clinical data in cancers via signeR 2.0
Authors: Drummond Rodrigo , Defelicibus Alexandre , Meyenberg Mathilde , Valieris Renan , Dias-Neto Emmanuel , Rosales Rafael A. , da Silva Israel Tojal 
Published: April 2023

AbstractMotivationCancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already became a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: 1)de novoestimation of signatures and their exposures, 2) estimation of the exposures of each one of a set of pre-defined signatures. Our group developed signeR, a Bayesian approach to both these tasks.ResultsHere we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes an user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded on the package for easy access.Availabilitysigner 2.0 is an open-source R package available through the Bioconductor project athttps://doi.org/doi:10.18129/B9.bioc.signeRContactitojal@accamargo.org.brorrrosales@usp.br
Vitamin D, chronic pain, and depression: linear and non-linear Mendelian randomization analyses
Authors: Bassett Emily , Gjekmarkaj Eva , Mason Amy M. , Zhao Sizheng Steven , Burgess Stephen 
Published: April 2023

ABSTRACTVitamin D deficiency has been linked to various chronic pain conditions. However, randomized trials of vitamin D supplementation have had mixed results. In contrast, systematic reviews of randomized trials indicate a protective effect of vitamin D supplementation on depression. We undertake a Mendelian randomization investigation in UK Biobank, a study of UK residents aged 40-65 at recruitment. We perform linear and non-linear Mendelian randomization analyses for four outcomes: fibromyalgia, clinical fatigue, chronic widespread pain, and probable lifetime major depression. We use genetic variants from four gene regions with known links to vitamin D biology as instruments. In linear analyses, genetically-predicted levels of 25-hydroxyvitamin D [25(OH)D], a clinical marker of vitamin D status, were not associated with fibromyalgia (odds ratio [OR] per 10 nmol/L higher 25(OH)D 1.02, 95% confidence interval [CI] 0.93, 1.12), clinical fatigue (OR 0.99, 95% CI 0.94, 1.05), chronic widespread pain (OR 0.95, 95% CI 0.89, 1.02), or probable lifetime major depression (OR 0.97, 95% CI 0.93, 1.01). In non-linear analyses, an association was observed between genetically-predicted 25(OH)D levels and depression in the quintile of the population with the lowest 25(OH)D levels (OR 0.78, 95% CI 0.64, 0.94); associations were null in other strata. Our findings suggest that population-wide vitamin D supplementation will not substantially reduce pain or depression; however, targeted supplementation of deficient individuals may reduce risk of depression.
Ambulatory Video EEG extended to 10 days: A retrospective review of a large database of ictal events
Authors: Wong Victoria , Hannon Timothy , Fernandes Kiran M. , Freestone Dean R. , Cook Mark J. , Nurse Ewan S. 
Published: April 2023

AbstractObjectiveThis work aims to determine the AVEM duration and number of captured seizures required to resolve different clinical questions, using a retrospective review of ictal recordings.MethodsPatients who underwent home-based AVEM had event data analyzed retrospectively. Studies were grouped by clinical indication: seizure differential diagnosis, classification, or treatment assessment. The proportion of studies where the conclusion was changed after the first seizure was determined, as was the AVEM duration needed for at least 99% of studies to reach a diagnostic conclusion.ResultsThe referring clinical question was not answered entirely by the first event in 29.56% (n=227) of studies. Diagnostic and classification indications required a minimum of 7 days for at least 99% of studies to be answered, whilst treatment-assessment required at least 6 days.ConclusionsAt least 7 days of monitoring, and potentially multiple events, are required to adequately answer these clinical questions in at least 99% of patients. The widely applied 72 hours or single event recording cut-offs may be insufficient to correctly answer these three indications in a substantial proportion of patients.SignificanceExtended duration of monitoring and capturing multiple events should be considered when attempting to capture seizures on AVEM.
Automatic segmentation and quantification of nigrosome-1 neuromelanin and iron in MRI: a candidate biomarker for Parkinson’s disease
Authors: Ariz Mikel , Martínez Martín , Alvarez Ignacio , Fernández-Seara Maria A. ,   The Catalonian Neuroimaging Parkinson’s disease Consortium, Pastor Pau , Pastor Maria A. , de Solórzano Carlos Ortiz 
Published: April 2023

AbstractThe dopaminergic neuronal loss in the substantia nigra pars compacta (SNc) has been related to a reduction of neuromelanin (NM) and accumulation of iron in the nigrosome-1 (N1) in Parkinson’s disease (PD). This suggests that N1 degeneration could be a promising early biomarker of PD. To date, only qualitative visual scales have been used to assess its degeneration in iron-sensitive images. Here we present the first fully-automatic method for the quantification of NM and iron content in the N1. Our method uses a multi-image atlas populated with healthy N1 structures that implements a customised label fusion strategy to segment the N1. NM-MRI and susceptibility-weighted images (SWI) of 71 PD patients and 30 healthy controls (HCs) were used in the study. Our quantification showed that N1’s NM content was reduced and the iron content increased in PD patients compared with HCs. ROC analyses showed the high diagnostic potential of N1, and revealed that the N1 alone was more sensitive than the entire SNc to detect abnormal iron accumulations in PD patients. Multi-parametric binary logistic regression showed that computer-assisted diagnosis methods could benefit from the segmentation of the N1 to boost their performance. A significant correlation was also found between most N1 image parameters and both disease duration and the motor status scored with the Unified Parkinson’s disease rating scale part III (UDPRS-III), suggesting a NM reduction along with an iron accumulation in N1 as the disease progresses. In addition, voxel-wise analyses revealed that this association was stronger for the N1 than for the entire SNc, highlighting the benefits of an accurate segmentation of the N1 to monitor disease course.
Depression at the intersection of race/ethnicity, sex/gender, and sexual orientation in a nationally representative sample of US adults: A design-weighted MAIHDA
Authors: McGuire F. Hunter , Beccia Ariel L. , Peoples JaNiene , Williams Matthew R. , Schuler Megan S. , Duncan Alexis E. 
Published: April 2023

ABSTRACTThis study examined how race/ethnicity, sex/gender, and sexual orientation intersect to socially pattern depression among US adults. We used repeated, cross-sectional data from the 2015-2020 National Survey on Drug Use and Health (NSDUH; n=234,772) to conduct design-weighted multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) for two outcomes: past-year and lifetime major depressive episode (MDE). With 42 intersectional groups constructed from seven race/ethnicity, two sex/gender, and three sexual orientation categories, we estimated group-specific prevalence and excess/reduced prevalence attributable to intersectional effects (i.e., two-way or higher interactions between identity variables). Models revealed heterogeneity between intersectional groups, with prevalence estimates ranging from 3.4–31.4% (past-year) and 6.7–47.4% (lifetime). Model main effects indicated that people who were Multiracial, White, women, gay/lesbian, or bisexual had greater odds of MDE. Additive effects of race/ethnicity, sex/gender, and sexual orientation explained most between-group variance; however, approximately 3% (past-year) and 12% (lifetime) were attributable to intersectional effects, with some groups experiencing excess/reduced prevalence. For both outcomes, sexual orientation main effects (42.9–54.0%) explained a greater proportion of between-group variance relative to race/ethnicity (10.0–17.1%) and sex/gender (7.5–7.9%). Notably, we extend MAIHDA to calculate nationally representative estimates to open future opportunities to quantify intersectionality with complex sample survey data.
The spatial distribution of coupling between tau and neurodegeneration in amyloid-β positive mild cognitive impairment
Authors: Robinson Belfin , Bhamidi Shankar , Dayan Eran ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: April 2023

ABSTRACTSynergies between amyloid-β (Aβ), tau, and neurodegeneration persist along the Alzheimer’s disease (AD) continuum. This study aimed to evaluate the extent of spatial coupling between tau and neurodegeneration (atrophy) and its relation to Aβ positivity in mild cognitive impairment (MCI). Data from 409 subjects were included (95 cognitively normal controls, 158 Aβ positive (Aβ+) MCI, and 156 Aβ negative (Aβ-) MCI) Florbetapir PET, Flortaucipir PET, and structural MRI were used as biomarkers for Aβ, tau and atrophy, respectively. Individual correlation matrices for tau load and atrophy were used to layer a multilayer network, with separate layers for tau and atrophy. A measure of coupling between corresponding regions of interest/nodes in the tau and atrophy layers was computed, as a function of Aβ positivity. The extent to which tau-atrophy coupling mediated associations between Aβ burden and cognitive decline was also evaluated. Heightened coupling between tau and atrophy in Aβ+ MCI was found primarily in the entorhinal and hippocampal regions (i.e., in regions corresponding to Braak stages I/II), and to a lesser extent in limbic and neocortical regions (i.e., corresponding to later Braak stages). Coupling strengths in the right middle temporal and inferior temporal gyri mediated the association between Aβ burden and cognition in this sample. Higher coupling between tau and atrophy in Aβ+ MCI is primarily evident in regions corresponding to early Braak stages and relates to overall cognitive decline. Coupling in neocortical regions is more restricted in MCI.