bluuebunny/crossref_metadata_2025_split huggingface dataset:
Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa
Authors: Nsubuga Mike , Galiwango Ronald , Jjingo Daudi , Mboowa Gerald 
Published: March 2024

Abstract
Background
Antimicrobial resistance (AMR) remains a significant global health threat particularly impacting low- and middle-income countries (LMICs). These regions often grapple with limited healthcare resources and access to advanced diagnostic tools. Consequently, there is a pressing need for innovative approaches that can enhance AMR surveillance and management. Machine learning (ML) though underutilized in these settings, presents a promising avenue. This study leverages ML models trained on whole-genome sequencing data from England, where such data is more readily available, to predict AMR in E. coli, targeting key antibiotics such as ciprofloxacin, ampicillin, and cefotaxime. A crucial part of our work involved the validation of these models using an independent dataset from Africa, specifically from Uganda, Nigeria, and Tanzania, to ascertain their applicability and effectiveness in LMICs.

Results
Model performance varied across antibiotics. The Support Vector Machine excelled in predicting ciprofloxacin resistance (87% accuracy, F1 Score: 0.57), Light Gradient Boosting Machine for cefotaxime (92% accuracy, F1 Score: 0.42), and Gradient Boosting for ampicillin (58% accuracy, F1 Score: 0.66). In validation with data from Africa, Logistic Regression showed high accuracy for ampicillin (94%, F1 Score: 0.97), while Random Forest and Light Gradient Boosting Machine were effective for ciprofloxacin (50% accuracy, F1 Score: 0.56) and cefotaxime (45% accuracy, F1 Score:0.54), respectively. Key mutations associated with AMR were identified for these antibiotics.

Conclusion
As the threat of AMR continues to rise, the successful application of these models, particularly on genomic datasets from LMICs, signals a promising avenue for improving AMR prediction to support large AMR surveillance programs. This work thus not only expands our current understanding of the genetic underpinnings of AMR but also provides a robust methodological framework that can guide future research and applications in the fight against AMR.
Health-related social needs information in the emergency department: clinician and patient perspectives on availability and use
Authors: Mazurenko Olena , Hirsh Adam T , Harle Christopher A , McNamee Cassidy , Vest Joshua R 
Published: March 2024

Abstract
Background
Patient health-related social needs (HRSN) complicate care and drive poor outcomes in emergency department (ED) settings. This study sought to understand what HRSN information is available to ED physicians and staff, and how HRSN-related clinical actions may or may not align with patient expectations.

Methods
We conducted a qualitative study using in-depth semi-structured interviews guided by HRSN literature, the 5 Rights of Clinical Decision Support (CDS) framework, and the Contextual Information Model. We asked ED providers, ED staff, and ED patients from one health system in the mid-Western United Stated about HRSN information availability during an ED encounter, HRSN data collection, and HRSN data use. Interviews were recorded, transcribed, and analyzed using modified thematic approach.

Results
We conducted 24 interviews (8 per group: ED providers, ED staff, and ED patients) from December 2022 to May 2023. We identified three themes: (1) Availability: ED providers and staff reported that HRSNs information is inconsistently available. The availability of HRSN data is influenced by patient willingness to disclose it during an encounter. (2) Collection: ED providers and staff preferred and predominantly utilized direct conversation with patients to collect HRSNs, despite other methods being available to them (e.g., chart review, screening questionnaires). Patients’ disclosure preferences were based on modality and team member. (3) Use: Patients wanted to be connected to relevant resources to address their HRSNs. Providers and staff altered clinical care to account for or accommodate HRSNs. System-level challenges (e.g., limited resources) limited provider and staff ability to address patients HRSNs.

Conclusions
In the ED, HRSNs information was inconsistently available, collected, or disclosed. Patients and ED providers and staff differed in their perspectives on how HSRNs should be collected and acted upon. Accounting for such difference in clinical and administrative decisions will be critical for patient acceptance and effective usage of HSRN information.
Evaluating CPR training: simulation vs. webinars for Iranian emergency medical technicians during COVID-19
Authors: Sarboozi-Hosseinabadi Shoaib , Sharifzadeh Gholamreza , Hosseini Seyed Mohammadreza 
Published: March 2024

AbstractIntroductionThe high prevalence of COVID-19 and the necessity for social distancing have impacted medical training. On the one hand, the high mortality rate following the disease led the American Heart Association (AHA) to issue guidelines in October 2020 for performing cardiopulmonary resuscitation on patients diagnosed or suspected of having COVID-19. Various methods exist for teaching these guidelines. However, the use of many of these methods is greatly challenged due to the high risk of disease transmission. Moreover, the published guidelines emphasize protection against COVID-19 infection. The present study aims to compare the impact of two educational methods, educational webinars and simulations, on the competence of performing cardiopulmonary resuscitation during the COVID-19 epidemic.MethodsThis semi-experimental study was conducted on 70 emergency medical technicians. A pre-test was administered to all participants, and then they were randomly assigned into two groups: an educational webinar group (35 people) and a simulation group (35 people). The educational webinar group received online training using Adobe Connect software version 2.6.9, while the simulation group received in-person training using a manikin simulator. The competence of performing cardiopulmonary resuscitation during the COVID-19 epidemic was compared between the two groups immediately after the training and again two months later. Data collection instruments utilized in this research included a demographic questionnaire and a competency questionnaire in performing CPR during the COVID-19 pandemic. The data were analyzed using SPSS software version 19 and statistical tests for comparison.ResultsThe results indicated that in both the educational webinar and simulation groups, the average competence score for performing cardiopulmonary resuscitation (CPR) at the three stages under investigation showed significant differences (p < 0.001). Additionally, in both groups under study, the average competence score for performing CPR immediately and two months after training was significantly higher compared to before the training (p < 0.001); however, two months after training, it was significantly lower compared to immediately after the training (p < 0.001).ConclusionsBased on the results obtained from the current research, both educational methods (educational webinar and simulation) had a significant effect on the competence of performing CPR during the COVID-19 epidemic and were equally effective. Moreover, the recall test results (two months later) showed a decrease in the competence of performing CPR during the COVID-19 epidemic in both training methods (webinar and simulation), indicating the need for periodic CPR training.
Utilization of EHRs for clinical trials: a systematic review
Authors: Kalankesh Leila R. , Monaghesh Elham 
Published: March 2024

Abstract
Background and objective
Clinical trials are of high importance for medical progress. This study conducted a systematic review to identify the applications of EHRs in supporting and enhancing clinical trials.

Materials and methods
A systematic search of PubMed was conducted on 12/3/2023 to identify relevant studies on the use of EHRs in clinical trials. Studies were included if they (1) were full-text journal articles, (2) were written in English, (3) examined applications of EHR data to support clinical trial processes (e.g. recruitment, screening, data collection). A standardized form was used by two reviewers to extract data on: study design, EHR-enabled process(es), related outcomes, and limitations.

Results
Following full-text review, 19 studies met the predefined eligibility criteria and were included. Overall, included studies consistently demonstrated that EHR data integration improves clinical trial feasibility and efficiency in recruitment, screening, data collection, and trial design.

Conclusions
According to the results of the present study, the use of Electronic Health Records in conducting clinical trials is very helpful. Therefore, it is better for researchers to use EHR in their studies for easy access to more accurate and comprehensive data. EHRs collects all individual data, including demographic, clinical, diagnostic, and therapeutic data. Moreover, all data is available seamlessly in EHR. In future studies, it is better to consider the cost-effectiveness of using EHR in clinical trials.
Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer
Authors: Qin Qiong , Gan Xiangyu , Lin Peng , Pang Jingshu , Gao Ruizhi , Wen Rong , Liu Dun , Tang Quanquan , Liu Changwen , He Yun , Yang Hong , Wu Yuquan 
Published: March 2024

Abstract
Objectives
To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model.

Materials and methods
This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training set of 74 and a validation set of 32 in a 7: 3 ratios. Ultrasomics features were extracted from the tumors’ region of interest of B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images based on PyRadiomics. Mann-Whitney U test, spearman, and least absolute shrinkage and selection operator algorithms were utilized to reduce features dimension. Five models were built with ultrasomics and clinical analysis using multilayer perceptron neural network classifier based on python. Including BUS, CEUS, Combined_1, Combined_2 and Clinical models. The diagnostic performance of models was assessed with the area under the curve (AUC) of the receiver operating characteristic. The DeLong testing algorithm was utilized to compare the models’ overall performance.

Results
The AUC (95% confidence interval [CI]) of the five models in the validation cohort were as follows: BUS 0.675 (95%CI: 0.481–0.868), CEUS 0.821 (95%CI: 0.660–0.983), Combined_1 0.829 (95%CI: 0.673–0.985), Combined_2 0.893 (95%CI: 0.780-1.000), and Clinical 0.690 (95%CI: 0.509–0.872). The Combined_2 model was the best in the overall prediction performance, showed significantly better compared to the Clinical model after DeLong testing (P < 0.01). Both univariate and multivariate logistic regression analyses showed that age (P < 0.01) and clinical stage (P < 0.01) could be an independent predictor of efficacy after nCRT in patients with LARC.

Conclusion
The ultrasomics model had better diagnostic performance to predict efficacy to nCRT in patients with LARC than the Clinical model.
Unified deep learning models for enhanced lung cancer prediction with ResNet-50–101 and EfficientNet-B3 using DICOM images
Authors: Kumar Vinod , Prabha Chander , Sharma Preeti , Mittal Nitin , Askar S. S. , Abouhawwash Mohamed 
Published: March 2024

AbstractSignificant advancements in machine learning algorithms have the potential to aid in the early detection and prevention of cancer, a devastating disease. However, traditional research methods face obstacles, and the amount of cancer-related information is rapidly expanding. The authors have developed a helpful support system using three distinct deep-learning models, ResNet-50, EfficientNet-B3, and ResNet-101, along with transfer learning, to predict lung cancer, thereby contributing to health and reducing the mortality rate associated with this condition. This offer aims to address the issue effectively. Using a dataset of 1,000 DICOM lung cancer images from the LIDC-IDRI repository, each image is classified into four different categories. Although deep learning is still making progress in its ability to analyze and understand cancer data, this research marks a significant step forward in the fight against cancer, promoting better health outcomes and potentially lowering the mortality rate. The Fusion Model, like all other models, achieved 100% precision in classifying Squamous Cells. The Fusion Model and ResNet-50 achieved a precision of 90%, closely followed by EfficientNet-B3 and ResNet-101 with slightly lower precision. To prevent overfitting and improve data collection and planning, the authors implemented a data extension strategy. The relationship between acquiring knowledge and reaching specific scores was also connected to advancing and addressing the issue of imprecise accuracy, ultimately contributing to advancements in health and a reduction in the mortality rate associated with lung cancer.
Preserving visual acuity: a compelling 12-year case study of controlling neovascular age-related macular degeneration
Authors: Tomás Rita O. , Campos António , Oliveira Nuno , Soares Pedro , Sousa João P. 
Published: March 2024

Abstract
Introduction
In neovascular age-related macular degeneration (nAMD) trials, anti-VEGF injection frequency decreases after the first year, while outcomes remain primarily related to the number of injections. To the best of our knowledge, there are no reports of maintaining the best corrected visual acuity (BCVA) for more than 7 years in extension studies.

Objective
To report a 12-year follow-up of a real-world case of nAMD where BCVA was preserved from declining.

Case description
A 67-year-old Caucasian female presented to our department in June 2010 due to decreased vision in her left eye (LE) within the preceding months. Examination showed a BCVA of 85 letters (L) in the right eye (RE) and 35 L in the LE. Fundus examination showed drusen in the macula of both eyes. Macular edema, loss of the macular lutein pigment, macular hypo/hyperpigmentation were observed in the LE. A diagnosis of Type 2 choroidal neovascular membrane (CNV) in the LE was established and within two months a Type 1 CNV developed in the RE. She undergone 9 injections of bevacizumab (six) and ranibizumab (three) within the first year of treatment in the LE and seven injections of ranibizumab within the first year in the RE.

Results
The LE had a mean of 5.2 injections per year, and the RE had a mean of 7.5 injections per year, from 2010 to 2022. RE's BCVA dropped by 8L (85L to 77L) and central retinal thickness (CRT) increased by 16 μm (276 μm to 292 μm) while LE’s BCVA increased by 28L (35L to 63L) and CRT decreased by 369 μm (680 μm to 311 μm), at the twelfth year.

Conclusions
Although the final visual outcome depends on baseline BCVA and lesion type or size, the number of injections is paramount in preserving BCVA and achieving favorable functional outcomes in nAMD, even after 12 years of treatment.
Actions targeting the integration of peer workforces in mental health organisations: a mixed-methods systematic review
Authors: Reeves Verity , McIntyre Heather , Loughhead Mark , Halpin Matthew Anthony , Procter Nicholas 
Published: March 2024

Abstract
Background
Lived experience workforces are one of the fastest growing emerging disciplines in Australian mental health service settings. Individuals with lived and living experience of mental distress employed in mental health services, often referred to as peer or lived experience workers, are widely considered essential for mental health recovery and reform. Despite vast growth of this workforce, concerns remain over the widespread integration of peer workforces to align with recommended movement of healthcare services toward greater recovery-orientated and person-centered practices. Previous research has identified barriers for peer work integration including a lack of clear role definition, inadequate training, and poor supportive organisational culture. Stigma, discrimination and a lack of acceptance by colleagues are also common themes. This systematic review seeks to identify organisational actions to support integration of peer workforces for improved mental health service delivery.

Method
A systematic search was conducted through online databases (n = 8) between January 1980 to November 2023. Additional data were sourced from conference proceedings, hand searching grey literature and scanning reference lists. Qualitative data was extracted and synthesised utilising narrative synthesis to identify key themes and findings reported adhere to PRISMA guidelines. The review protocol was registered with Prospero (CRD: 42,021,257,013).

Results
Four key actions were identified: education and training, organisational readiness, Structural adjustments, resourcing and support and, demonstrated commitment to peer integration and recovery practice.

Conclusions
The study identifies actions for mental health service organisations and system leaders to adopt in support of integrating peer and lived experience workforces in service delivery.
Reliability and validity of the Patient Health Questionnaire-4 scale and its subscales of depression and anxiety among US adults based on nativity
Authors: Adzrago David , Walker Timothy J. , Williams Faustine 
Published: March 2024

Abstract
Background
The burdens of anxiety and depression symptoms have significantly increased in the general US population, especially during this COVID-19 epidemiological crisis. The first step in an effective treatment for anxiety and depression disorders is screening. The Patient Health Questionnaire-4 (PHQ-4, a 4-item measure of anxiety/depression) and its subscales (PHQ-2 [a 2-item measure of depression] and Generalized Anxiety Disorder [GAD-2, a 2-item measure of anxiety]) are brief but effective mass screening instruments for anxiety and depression symptoms in general populations. However, little to no study examined the psychometric properties (i.e., reliability and validity) of the PHQ-4 and its subscales (PHQ-2 and GAD-2) in the general US adult population or based on US nativity (i.e., foreign-born vs. the US-born). We evaluated the psychometric properties of the PHQ-4 and its subscales in US adults, as well as the psychometric equivalence of the PHQ-4 scale based on nativity.

Methods
We conducted a cross-sectional survey of 5,140 adults aged ≥ 18 years. We examined the factorial validity and dimensionality of the PHQ-4 with confirmatory factor analysis (CFA). A multiple-group confirmatory factor analysis (MCFA) was used to evaluate the comparability of the PHQ-4 across nativity groups. Reliability indices were assessed. Also, the scales’ construct validities were assessed by examining the associations of both the PHQ-4 and its subscales’ scores with the sociodemographic characteristics and the 3-item UCLA Loneliness scale.

Results
The internal consistencies were high for the PHQ-4 scale (α = 0.92) and its subscales of PHQ-2 (α = 0.86) and GAD-2 (α = 0.90). The CFA fit indices showed evidence for the two-factor structure of the PHQ-4. The two factors (i.e., anxiety and depression) were significantly correlated (r = 0.92). The MCFA demonstrated measurement invariance of the PHQ-4 across the nativity groups, but the model fits the data better in the foreign-born group. There were significant associations of the PHQ-4 scale and its subscales’ scores with the sociodemographic characteristics and the UCLA Loneliness scale (all p < 0.001).

Conclusions
The PHQ-4 and its subscales are reliable and valid measures to screen anxiety and depression symptoms in the general US adult population, especially in foreign-born individuals during the COVID-19 pandemic.
Incorporating social vulnerability in infectious disease mathematical modelling: a scoping review
Authors: Naidoo Megan , Shephard Whitney , Kambewe Innocensia , Mtshali Nokuthula , Cope Sky , Rubio Felipe Alves , Rasella Davide 
Published: March 2024

Abstract
Background
Highlighted by the rise of COVID-19, climate change, and conflict, socially vulnerable populations are least resilient to disaster. In infectious disease management, mathematical models are a commonly used tool. Researchers should include social vulnerability in models to strengthen their utility in reflecting real-world dynamics. We conducted a scoping review to evaluate how researchers have incorporated social vulnerability into infectious disease mathematical models.

Methods
The methodology followed the Joanna Briggs Institute and updated Arksey and O'Malley frameworks, verified by the PRISMA-ScR checklist. PubMed, Clarivate Web of Science, Scopus, EBSCO Africa Wide Information, and Cochrane Library were systematically searched for peer-reviewed published articles. Screening and extracting data were done by two independent researchers.

Results
Of 4075 results, 89 articles were identified. Two-thirds of articles used a compartmental model (n = 58, 65.2%), with a quarter using agent-based models (n = 24, 27.0%). Overall, routine indicators, namely age and sex, were among the most frequently used measures (n = 42, 12.3%; n = 22, 6.4%, respectively). Only one measure related to culture and social behaviour (0.3%). For compartmental models, researchers commonly constructed distinct models for each level of a social vulnerability measure and included new parameters or influenced standard parameters in model equations (n = 30, 51.7%). For all agent-based models, characteristics were assigned to hosts (n = 24, 100.0%), with most models including age, contact behaviour, and/or sex (n = 18, 75.0%; n = 14, 53.3%; n = 10, 41.7%, respectively).

Conclusions
Given the importance of equitable and effective infectious disease management, there is potential to further the field. Our findings demonstrate that social vulnerability is not considered holistically. There is a focus on incorporating routine demographic indicators but important cultural and social behaviours that impact health outcomes are excluded. It is crucial to develop models that foreground social vulnerability to not only design more equitable interventions, but also to develop more effective infectious disease control and elimination strategies. Furthermore, this study revealed the lack of transparency around data sources, inconsistent reporting, lack of collaboration with local experts, and limited studies focused on modelling cultural indicators. These challenges are priorities for future research.