bluuebunny/crossref_metadata_2025_split huggingface dataset:
CalScope: methodology and lessons learned for conducting a remote statewide SARS-CoV-2 seroprevalence study in California using an at-home dried blood spot collection kit and online survey
Authors: Lim Esther , Mehrotra Megha L. , Lamba Katherine , Kamali Amanda , Lai Kristina W. , Meza Erika , Bertsch-Merbach Stephanie , Szeto Irvin , Ley Catherine , Martin Andrew B. , Parsonnet Julie , Robinson Peter , Gebhart David , Fonseca Noemi , Tsai Cheng-ting , Seftel David , Nicolici Allyx , Melton David , Jain Seema 
Published: May 2024

Abstract
Background
To describe the methodology for conducting the CalScope study, a remote, population-based survey launched by the California Department of Public Health (CDPH) to estimate SARS-CoV-2 seroprevalence and understand COVID-19 disease burden in California.

Methods
Between April 2021 and August 2022, 666,857 randomly selected households were invited by mail to complete an online survey and at-home test kit for up to one adult and one child. A gift card was given for each completed survey and test kit. Multiple customized REDCap databases were used to create a data system which provided task automation and scalable data management through API integrations. Support infrastructure was developed to manage follow-up for participant questions and a communications plan was used for outreach through local partners.

Results
Across 3 waves, 32,671 out of 666,857 (4.9%) households registered, 6.3% by phone using an interactive voice response (IVR) system and 95.7% in English. Overall, 25,488 (78.0%) households completed surveys, while 23,396 (71.6%) households returned blood samples for testing. Support requests (n = 5,807) received through the web-based form (36.3%), by email (34.1%), and voicemail (29.7%) were mostly concerned with the test kit (31.6%), test result (26.8%), and gift card (21.3%).

Conclusions
Ensuring a well-integrated and scalable data system, responsive support infrastructure for participant follow-up, and appropriate academic and local health department partnerships for study management and communication allowed for successful rollout of a large population-based survey. Remote data collection utilizing online surveys and at-home test kits can complement routine surveillance data for a state health department.
Professional beliefs of physicians and allied health professionals and their willingness to promote health in primary care: a cross-sectional survey
Authors: Brandt Sophie Karoline , Essig Stefan , Balthasar Andreas 
Published: May 2024

Abstract
Background
Primary care professionals could play a key role in health promotion implementation. A fundamental aspect that might affect the willingness of primary care professionals to strengthen health promotion, and about which we do not yet know much, are professional beliefs. Therefore, we conducted a quantitative survey to (1) compare professional beliefs and the willingness to work more in health promotion between five major primary care professions, and (2) investigate associations between professional beliefs and the willingness to work more in health promotion.

Methods
A large-scale cross-sectional study based on a nation-wide web-based survey of primary care professionals in Switzerland was conducted from January to July 2022. The survey was addressed to pharmacists, physicians, medical practice assistants, nurses, and physiotherapists working in primary care in Switzerland. Differences between groups were tested using T-tests and Chi-square tests. Multivariable logistic regression analyses were used to evaluate the association between variables related to professional beliefs and the willingness to work more in health promotion.

Results
The responses of 4’063 primary care professionals were used for analysis. Most primary care professionals revealed a salutogenetic attitude towards their primary care tasks. Members of all professions showed high awareness of their tasks in tackling increased risks of disease (80.2% of all participants). Especially allied health professionals wished to see a greater role of prevention in primary care (pharmacists: 72.4%, medical practice assistants: 63.9%, nurses: 75.6%, physiotherapists: 73.9% versus physicians: 46.9%). All professional groups showed a high willingness to work more in health promotion (88% of all participants). Salutogenetic beliefs of primary care professionals and their willingness to work more in health promotion are strongly associated. Participants agreeing that health promotion should play a greater role or that preventive consultations should be offered in primary care, are more willing to work more in health promotion compared to participants who disagree with these ideas.

Conclusions
Both affiliation to allied primary care professions and salutogenetic professional beliefs are associated with higher willingness to work more in health promotion. The high willingness provides evidence of a large, yet untapped potential. Promoting salutogenetic beliefs might further increase the willingness to engage in health promotion.
Person-centred medicine in the care home setting: development of a complex intervention
Authors: Høj Kirsten , Bekker Hilary Louise , Bro Flemming , Olesen Anne Estrup , Kristensen Jette Kolding , Christensen Line Due 
Published: May 2024

Abstract
Background
Person-centred medicine is recommended in the care of older patients. Yet, involvement of care home residents and relatives in medication processes remains limited in routine care. Therefore, we aimed to develop a complex intervention focusing on resident and relative involvement and interprofessional communication to support person-centred medicine in the care home setting.

Methods
The development took place from October 2021 to March 2022 in the Municipality of Aarhus, Denmark. The study followed the Medical Research Council guidance on complex intervention development using a combination of theoretical, evidence-based, and partnership approaches. The patient involvement tool, the PREparation of Patients for Active Involvement in medication Review (PREPAIR), was included in a preliminary intervention model. Study activities included developing programme theory, engaging stakeholders, and exploring key uncertainties through interviews, co-producing workshops, and testing with end-users to develop the intervention and an implementation strategy. The Consolidated Framework for Implementation Research and the Interprofessional Shared Decision Making Model were used. Data were analysed using a rapid analysis approach.

Results
Before the workshops, six residents and four relatives were interviewed. Based on their feedback, PREPAIR was modified to the PREPAIR care home to fit the care home population. In total, ten persons participated in the co-producing workshops, including health care professionals and municipal managerial and quality improvement staff. The developed intervention prototype was tested for three residents and subsequently refined to the final intervention, including two fixed components (PREPAIR care home and an interprofessional medication communication template) delivered in a flexible three-stage workflow. Additionally, a multi-component implementation strategy was formed. In line with the developed programme theory, the intervention supported health care professionals´ awareness about resident and relative involvement. It provided a structure for involvement, empowered the residents to speak, and brought new insights through dialogue, thereby supporting involvement in medication-related decisions. The final intervention was perceived to be relevant, acceptable, and feasible in the care home setting.

Conclusion
Our results indicate that the final intervention may be a viable approach to facilitate person-centred medicine through resident and relative involvement. This will be further explored in a planned feasibility study.
The relationship and pathways between resting-state EEG, physical function, and cognitive function in older adults
Authors: Liu Hairong , Wang Jing , Xin Xin , Wang Peng , Jiang Wanting , Meng Tao 
Published: May 2024

Abstract
Objective
Based on resting-state electroencephalography (EEG) evidence, this study aimed to explore the relationship and pathways between EEG-mediated physical function and cognitive function in older adults with cognitive impairment.

Methods
A total of 140 older adults with cognitive impairment were recruited, and data on their physical function, cognitive function, and EEG were collected. Pearson correlation analysis, one-way analysis of variance, linear regression analysis, and structural equation modeling analysis were conducted to explore the relationships and pathways among variables.

Results
FP1 theta (effect size = 0.136, 95% CI: 0.025–0.251) and T4 alpha2 (effect size = 0.140, 95% CI: 0.057–0.249) were found to significantly mediate the relationship. The direct effect (effect size = 0.866, 95% CI: 0.574–1.158) and total effect (effect size = 1.142, 95% CI: 0.848–1.435) of SPPB on MoCA were both significant.

Conclusion
Higher physical function scores in older adults with cognitive impairment were associated with higher cognitive function scores. Left frontal theta and right temporal alpha2, as key observed indicators, may mediate the relationship between physical function and cognitive function. It is suggested to implement personalized exercise interventions based on the specific physical function of older adults, which may delay the occurrence and progression of cognitive impairment in older adults with cognitive impairment.
The role of sarcopenia in fragility fractures of the pelvis – is sarcopenia an underestimated risk factor?
Authors: Mair Olivia , Neumann Jan , Rittstieg Philipp , Müller Michael , Biberthaler Peter , Hanschen Marc 
Published: May 2024

Abstract
Background
Fragility fractures of the pelvis (FFPs) represent a significant health burden, particularly for the elderly. The role of sarcopenia, an age-related loss of muscle mass and function, in the development and impact of these fractures is not well understood. This study aims to investigate the prevalence and impact of osteoporosis and sarcopenia in patients presenting with FFPs.

Methods
This retrospective study evaluated 140 elderly patients with FFPs. The diagnosis of sarcopenia was assessed by psoas muscle area (PMA) and the height-adjusted psoas muscle index (PMI) measured on computed tomography (CT) scans. Clinical data, radiological findings and functional outcomes were recorded and compared with the presence or absence of sarcopenia and osteoporosis.

Results
Our study cohort comprised 119 female (85.0%) and 21 (15.0%) male patients. The mean age at the time of injury or onset of symptoms was 82.26 ± 8.50 years.
Sarcopenia was diagnosed in 68.6% (n = 96) patients using PMA and 68.8% (n = 88) using PMI. 73.6% (n = 103) of our study population had osteoporosis and 20.0% (n = 28) presented with osteopenia. Patients with sarcopenia and osteoporosis had longer hospital stays (p < 0.04), a higher rate of complications (p < 0.048) and functional recovery was significantly impaired, as evidenced by a greater need for assistance in daily living (p < 0.03). However, they were less likely to undergo surgery (p < 0.03) and the type of FFP differed significantly (p < 0.04). There was no significant difference in mortality rate, pre-hospital health status, age or gender.

Conclusion
Our study highlights the important role of sarcopenia in FFPs in terms of the serious impact on health and quality of life in elderly patients especially when osteoporosis and sarcopenia occur together.
Identifying and targeting sarcopenia in older patients may be an important strategy to reduce pelvic fractures and improve recovery. Further research is needed to develop effective prevention and treatment approaches that target muscle health in the elderly.
A cross-sectional study of SARS-CoV-2 antibodies among healthcare workers in a tertiary care hospital in Taiwan: implications for protection against the Omicron variants
Authors: Chen Chang-Hua , Chao Day-Yu , Kor Chew-Teng , Kuo Su-Feng , Lin Jen-Shiou , Lai Huei-Wen , Liu Yen-Tze , Lin Ching-Hsiung , Chen Mu-Kuan 
Published: May 2024

Abstract
Background
Taiwan, deeply impacted by the 2003 SARS outbreak, promptly implemented rigorous infection control and prevention (ICP) measures in January 2020 to combat the global COVID-19 pandemic. This cross-sectional serologic study was conducted among healthcare workers (HCWs) in a tertiary care hospital in Taiwan from August 1, 2022, to February 28, 2023. The study aimed to assess HCWs’ antibody responses to COVID-19 vaccination against Omicron subvariants BA.1, BA.4, and BA.5, considering variations in prior infection. Additionally, it evaluated the effectiveness of ICP and vaccination policies within the hospital setting in Taiwan.

Methods
A cross-sectional serology study was conducted in Taiwan to investigate the seroprevalence rates of Omicron subvariants BA.1, BA.4, and BA.5 among HCWs. A total of 777 HCWs participated in this study. A structured questionnaire was collected to obtain the epidemiological characteristics and risk factors for potential exposure. Enzyme-linked immunosorbent assay was used to detect antibody responses. Serum samples were selected for protection against Omicron subvariants BA.1, BA.4, and BA.5 by using a pseudotyped-based neutralization assay.

Results
More than 99% of the participants had received SARS-CoV-2 vaccination. Overall, 57.7% had been infected with SARS-CoV-2, with some being asymptomatic. The SARS-CoV-2 Anti-Spike S1 protein IgG (Anti-S) distribution was 40,000 AU/mL for 20.2% (157/777) of participants, with a mean ± standard deviation of 23,442 ± 22,086. The decay curve for Anti-S was less than 20,000 AU/ml after 120 days. The probability curve of 50% neutralization showed an Anti-S of 55,000 AU/ml. The optimum Anti-S was 41,328 AU/mL (equal to 5,869 WHO’s standard BAU/mL), with 86.1% sensitivity and 63.5% specificity.

Conclusions
In this significant study, 20.2% of HCWs achieved seroprotection against Omicron subvariants BA.1, BA.4, and BA.5. Their immunity against Omicron subvariants was further reinforced through recommended vaccinations and the development of natural immunity from SARS-CoV-2 exposure, collectively enhancing their protection against Omicron.
Predictors of unsuccessful tuberculosis treatment outcomes in Brazil: an analysis of 259,484 patient records
Authors: Ryuk Do Kyung , Pelissari Daniele M. , Alves Kleydson , Oliveira Patricia Bartholomay , Castro Marcia C. , Cohen Ted , Sanchez Mauro , Menzies Nicolas A. 
Published: May 2024

Abstract
Introduction
Tuberculosis (TB) causes over 1 million deaths annually. Providing effective treatment is a key strategy for reducing TB deaths. In this study, we identified factors associated with unsuccessful treatment outcomes among individuals treated for TB in Brazil.

Methods
We obtained data on individuals treated for TB between 2015 and 2018 from Brazil’s National Disease Notification System (SINAN). We excluded patients with a history of prior TB disease or with diagnosed TB drug resistance. We extracted information on patient-level factors potentially associated with unsuccessful treatment, including demographic and social factors, comorbid health conditions, health-related behaviors, health system level at which care was provided, use of directly observed therapy (DOT), and clinical examination results. We categorized treatment outcomes as successful (cure, completed) or unsuccessful (death, regimen failure, loss to follow-up). We fit multivariate logistic regression models to identify factors associated with unsuccessful treatment.

Results
Among 259,484 individuals treated for drug susceptible TB, 19.7% experienced an unsuccessful treatment outcome (death during treatment 7.8%, regimen failure 0.1%, loss to follow-up 11.9%). The odds of unsuccessful treatment were higher with older age (adjusted odds ratio (aOR) 2.90 [95% confidence interval: 2.62–3.21] for 85-100-year-olds vs. 25-34-year-olds), male sex (aOR 1.28 [1.25–1.32], vs. female sex), Black race (aOR 1.23 [1.19–1.28], vs. White race), no education (aOR 2.03 [1.91–2.17], vs. complete high school education), HIV infection (aOR 2.72 [2.63–2.81], vs. no HIV infection), illicit drug use (aOR 1.95 [1.88–2.01], vs. no illicit drug use), alcohol consumption (aOR 1.46 [1.41–1.50], vs. no alcohol consumption), smoking (aOR 1.20 [1.16–1.23], vs. non-smoking), homelessness (aOR 3.12 [2.95–3.31], vs. no homelessness), and immigrant status (aOR 1.27 [1.11–1.45], vs. non-immigrants). Treatment was more likely to be unsuccessful for individuals treated in tertiary care (aOR 2.20 [2.14–2.27], vs. primary care), and for patients not receiving DOT (aOR 2.35 [2.29–2.41], vs. receiving DOT).

Conclusion
The risk of unsuccessful TB treatment varied systematically according to individual and service-related factors. Concentrating clinical attention on individuals with a high risk of poor treatment outcomes could improve the overall effectiveness of TB treatment in Brazil.
A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease
Authors: Ma Ze-Peng , Wang Shi-Wei , Xue Lin-Yan , Zhang Xiao-Dan , Zheng Wei , Zhao Yong-Xia , Yuan Shuang-Rui , Li Gao-Yang , Yu Ya-Nan , Wang Jia-Ning , Zhang Tian-Le 
Published: May 2024

Abstract
Background
The prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension.

Methods
132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity.

Results
The feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively.

Conclusions
Radiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD.
Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies
Authors: Alrashdi Ibrahim 
Published: May 2024

AbstractThe quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases in smart cities, Artificial Intelligence of Things (AIoT) technology, based on the integration of artificial intelligence (AI) with the Internet of Things (IoT), is commonly used to promote efficient control and diagnosis during the outbreak, thereby minimizing possible losses. However, the presence of multi-source institutional data remains one of the major challenges hindering the practical usage of AIoT solutions for pandemic disease diagnosis. This paper presents a novel framework that utilizes multi-site data fusion to boost the accurateness of pandemic disease diagnosis. In particular, we focus on a case study of COVID-19 lesion segmentation, a crucial task for understanding disease progression and optimizing treatment strategies. In this study, we propose a novel multi-decoder segmentation network for efficient segmentation of infections from cross-domain CT scans in smart cities. The multi-decoder segmentation network leverages data from heterogeneous domains and utilizes strong learning representations to accurately segment infections. Performance evaluation of the multi-decoder segmentation network was conducted on three publicly accessible datasets, demonstrating robust results with an average dice score of 89.9% and an average surface dice of 86.87%. To address scalability and latency issues associated with centralized cloud systems, fog computing (FC) emerges as a viable solution. FC brings resources closer to the operator, offering low latency and energy-efficient data management and processing. In this context, we propose a unique FC technique called PANDFOG to deploy the multi-decoder segmentation network on edge nodes for practical and clinical applications of automated COVID-19 pneumonia analysis. The results of this study highlight the efficacy of the multi-decoder segmentation network in accurately segmenting infections from cross-domain CT scans. Moreover, the proposed PANDFOG system demonstrates the practical deployment of the multi-decoder segmentation network on edge nodes, providing real-time access to COVID-19 segmentation findings for improved patient monitoring and clinical decision-making.
Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study
Authors: Ge Sihan , Chen Junxin , Wang Wei , Zhang Li-bo , Teng Yue , Yang Cheng , Wang Hao , Tao Yihao , Chen Zhi , Li Ronghao , Niu Yin , Zuo Chenghai , Tan Liang 
Published: May 2024

Abstract
Background
Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results.

Methods
This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature.

Results
A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model.

Conclusions
In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.