Medical Plan Research Articles

Inherited privilege? First vs. continuing-generation medical students in Egypt, academic performance, extracurricular training and expectations: a cross-sectional study.

The transition to college life is a highly demanding period for all students, especially when enrolling into an academically-challenging field as that of medicine. First-generation medical students represent an overlooked demographic and are thought to be more vulnerable to the stressors of college. This study's aim is to explore the differences between first and continuing-generation medical students in Egypt regarding their academic performance, extracurricular training opportunities and future expectations. Two identical 24-item online questionnaires, in English and Arabic were distributed with five main themes: general demographics, academic performance, extracurricular training opportunities (outpatient clinics, hospitals and operating rooms), future expectations and if participants have relatives in the medical field. Responses from 1652 eligible respondents from all 36 medical universities in Egypt were received. Continuing-generation medical students achieved a higher cumulative grade, with 46.1% getting an "Excellent" grade compared to only 38.4% in the first-generation. The gap between the two groups was best noted in Year 1 (p-value < 0.001), as continuing-generation medical students were almost twice more likely to score an "Excellent" grade [Odds Ratio = 1.85 (1.48-2.31)]. First-generation group had less training opportunities in clinics (p < 0.001) and operating rooms (p < 0.001). There was a significant difference (p < 0.001) across all three clinical settings in regard to how the training opportunity was acquired. The majority of continuing-generation medical students credited their family members for it. Amongst those who wish to travel, the majority of the first-generation medical students plan to pursue their career in the United States; whereas, most of continuing-generation medical students aimed for the Middle East and Western Europe. First-generation medical students had lower grades at first mainly due to their lack of awareness of proper resources that were provided by the families of continuing-generation medical students. However, the challenges posed to first-generation medical students prompted the development of higher resilience, enabling them to catch up and even surpass their continuing-generation counterparts. Continuing-generation medical students obtained further extracurricular training (outpatient clinics and operating rooms), accessed mainly through their family members and connections; whereas, first-generation medical students received most of their training by searching for it themselves.

AI’s pivotal impact on redefining stakeholder roles and their interactions in medical education and health care

Artificial Intelligence (AI) has the potential to revolutionize medical training, diagnostics, treatment planning, and healthcare delivery while also bringing challenges such as data privacy, the risk of technological overreliance, and the preservation of critical thinking. This manuscript explores the impact of AI and Machine Learning (ML) on healthcare interactions, focusing on faculty, students, clinicians, and patients. AI and ML's early inclusion in the medical curriculum will support student-centered learning; however, all stakeholders will require specialized training to bridge the gap between medical practice and technological innovation. This underscores the importance of education in the ethical and responsible use of AI and emphasizing collaboration to maximize its benefits. This manuscript calls for a re-evaluation of interpersonal relationships within healthcare to improve the overall quality of care and safeguard the welfare of all stakeholders by leveraging AI's strengths and managing its risks.

Availability Of Adult Dental Plans In The Affordable Care Act Marketplaces, 2016-23.

The Affordable Care Act Marketplaces may improve access to dental insurance, but little is known about the availability of such coverage. We used data from state and federal Marketplace sources to describe the availability of adult dental plans, including Stand-alone Dental Plans and those embedded in medical plans. We also examined the characteristics of counties with limited dental insurer participation and those that experienced a net loss of Stand-alone Dental Plan insurers between 2016 and 2023. We found that in 2023, at least one Stand-alone Dental Plan was offered in every state, and thirty-six states offered embedded dental plans. Most counties (63.6percent) had access to more than five insurers offering adult dental plans, whereas approximately 4percent had only one insurer offering adult dental plans. Counties in state-based Marketplaces, rural areas, and dentist shortage areas were more likely to be counties with limited dental insurer participation. The net loss of Stand-alone Dental Plans between 2016 and 2023 was more common in state-based Marketplaces and disadvantaged counties. Our findings can inform future policies to improve the dental insurance Marketplace and access to affordable dental care.

Accuracy is inaccurate: Why a focus on diagnostic accuracy for medical chatbot AIs will not lead to improved health outcomes.

Since its launch in November 2022, ChatGPT has become a global phenomenon, sparking widespread public interest in chatbot artificial intelligences (AIs) generally. While not approved for medical use, it is capable of passing all three United States medical licensing exams and offers diagnostic accuracy comparable to a human doctor. It seems inevitable that it, and tools like it, are and will be used by the general public to provide medical diagnostic information or treatment plans. Before we are taken in by the promise of a golden age for chatbot medical AIs, it would be wise to consider the implications of using these tools as either supplements to, or substitutes for, human doctors. With the rise of publicly available chatbot AIs, there has been a keen focus on research into the diagnostic accuracy of these tools. This, however, has left a notable gap in our understanding of the implications for health outcomes of these tools. Diagnosis accuracy is only part of good health care. For example, crucial to positive health outcomes is the doctor-patient relationship. This paper challenges the recent focus on diagnostic accuracy by drawing attention to the causal relationship between doctor-patient relationships and health outcomes arguing that chatbot AIs may even hinder outcomes in numerous ways including subtracting the elements of perception and observation that are crucial to clinical consultations. The paper offers brief suggestions to improve chatbot medical AIs so as to positively impact health outcomes.

The Oncology QCARD Initiative: Fostering efficient evaluation of initial real-world data proposals.

The oncology quality, characterization, and assessment of real-world data (Oncology QCARD) Initiative was formed to develop a set of minimum study design and data elements needed to evaluate the fitness of the real-world data (RWD) source(s) proposed in an initial study concept as part of early interaction with scientific reviewers. A multidisciplinary executive committee (EC) was established to guide the Oncology QCARD Initiative. The EC conducted a landscape review of published literature, guidances, and guidelines to evaluate relevant dimensions of data quality measurement. Guided by the review and informed by expert feedback, the Oncology QCARD Initial Protocol Characterization (IPC) provides a summary of minimum elements needed to adequately describe an initial clinical study concept that involves RWD and is intended to support decision-making. Fit-for-use data and fit-for-purpose design emerged as themes from the landscape analysis. Data that are fit-for-use are both relevant (sufficiently capturing exposure, outcomes, and covariates) and reliable (understanding data accrual and quality control and whether the data represent the underlying concepts they are intended to represent) to answer a specific research question. A fit-for-purpose design takes appropriate steps to ensure internal and external validity and allows for transparency in reporting. The QCARD-IPC focuses on high-level characteristics of RWD sources and study design domains including data temporality, population, medical product exposure, comparators, and covariates, endpoints, statistical analysis, and data quality assurance plans. Evaluation of studies including RWD requires understanding the data source, study design, and potential biases to preliminarily evaluate whether selected RWD are fit-for-use for the research question. The Oncology QCARD-IPC provides a structured, transparent approach to facilitate early review and enhanced communication between study sponsors and scientific reviewers of initial study proposals including RWD.

Fuzzy Soft Sets and its Application to Decision Making: A Short Case Study Involving the Health Sector

The health sector faces uncertainty and complex decision-making scenarios, making traditional analytical tools insufficient. The fuzzy soft set theory has emerged as a powerful framework for modeling and reasoning with uncertain information, with promising applications in the health domain. This project explores the application of fuzzy soft sets in various decision-making processes in the health sector, including medical diagnosis, disease classification, treatment planning, risk assessment, patient stratification, and predictive modeling. The study reviews historical development of fuzzy set theory and its extension to soft sets, discussing challenges, limitations, and future research directions. The findings aim to contribute to the growing body of knowledge on the practical relevance and potential of fuzzy soft set theory in addressing healthcare decision-making needs.

Noise-assisted hybrid attention networks for low-dose PET and CT denoising.

Positron emission tomography (PET) and computed tomography (CT) play a vital role in tumor-related medical diagnosis, assessment, and treatment planning. However, full-dose PET and CT pose the risk of excessive radiation exposure to patients, whereas low-dose images compromise image quality, impacting subsequent tumor recognition and diseasediagnosis. To solve such problems, we propose a Noise-Assisted Hybrid Attention Network (NAHANet) to reconstruct full-dose PET and CT images from low-dose PET (LDPET) and CT (LDCT) images to reduce patient radiation risks while ensuring the performance of subsequent tumor recognition. NAHANet contains two branches: the noise feature prediction branch (NFPB) and the cascaded reconstruction branch. Among them, NFPB providing noise features for the cascade reconstruction branch. The cascaded reconstruction branch comprises a shallow feature extraction module and a reconstruction module which contains a series of cascaded noise feature fusion blocks (NFFBs). Among these, the NFFB fuses the features extracted from low-dose images with the noise features obtained by NFPB to improve the feature extraction capability. To validate the effectiveness of the NAHANet method, we performed experiments using two public available datasets: the Ultra-low Dose PET Imaging Challenge dataset and Low Dose CT Grand Challengedataset. As a result, the proposed NAHANet achieved higher performance on common indicators. For example, on the CT dataset, the PSNR and SSIM indicators were improved by 4.1dB and 0.06 respectively, and the rMSE indicator was reduced by 5.46 compared with the LDCT; on the PET dataset, the PSNR and SSIM was improved by 3.37dB and 0.02, and the rMSE was reduced by 9.04 compared with theLDPET. This paper proposes a transformer-based denoising algorithm, which utilizes hybrid attention to extract high-level features of low dose images and fuses noise features to optimize the denoising performance of the network, achieving good performance improvements on low-dose CT and PETdatasets.

Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease.

Identifying individuals with early-stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would enable better-informed medical, support, and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau positron emission tomography (PET) in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. Across heterogeneous clinical phenotypes, patients with atypical AD consistently exhibit abnormal tau accumulation in the posterior nodes of the default mode network of the cerebral cortex. This evidence suggests that tau burden in this functional network could be a common imaging biomarker for prognostication across the syndromic spectrum of AD. Here, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A+/T+/N+ patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes: Posterior Cortical Atrophy (n = 16), logopenic variant Primary Progressive Aphasia (n = 15), and amnestic syndrome with multi-domain impairment and young age of onset < 65 years (n = 17). All patients underwent magnetic resonance imaging (MRI), tau PET, and amyloid PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Atypical early AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t(47) = 6.56, p < .001, Cohen's d = 0.95. Across clinical phenotypes, baseline tau in the default mode network was the strongest predictor of clinical decline (R2 = .30), outperforming a simpler model with baseline clinical impairment and demographic variables (R2 = .10), tau in other functional networks (R2 = .11-.26), and the magnitude of cortical atrophy (R2 = .20) and amyloid burden (R2 = .09) in the default mode network. Overall, these findings point to the contribution of default mode network tau to predicting the magnitude of clinical decline in atypical early AD patients one year later. This simple measure could aid the development of a personalized prognostic, monitoring, and treatment plan, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.

Explainable AI for Transparent MRI Segmentation: Deep Learning and Visual Attribution in Clinical Decision Support

For medical diagnosis and therapy planning, the importance of accurate MRI segmentation cannot be overemphasized. Conversely, the inscrutability of deep learning models remains obstacles to their application in therapeutic contexts. In this article, an interpretability artificial intelligence framework is introduced. It combines an MRI segmentation model based on deep learning, visual attribution algorithms and natural language explanations. EXPERIMENT The dataset is consisting of plenty of different types of brain MRI scans, and used to test the architecture. The average of Dice score of our method is 88.7% and 92.3% for segmentation of tumor and categorization of tissues, respectively. Both are pretty epic scores. The insights extracted from both the visual attributions and textual explations improve our understanding of how the model arrives at its decisions, thereby increasing the transparency and interpretability of the model. believe this approach to explainable artificial intelligence can help to close the gap between state-of-the-art performance in MRI segmentation and clinical interpretability, by increasing the transparency of complex models and facilitating their implementation into a clinical workflow. Conclusion Our approach may have implications in the transparent and reliable development of AI-based decision support systems for medical imaging

Incidence of an Identifiable Organism in Children Who Underwent a Surgical Procedure for Granulomatous Cervical Lymphadenopathy.

Objectives: The incidence of cervical lymphadenopathy due to nontuberculous mycobacteria is rising in the pediatric population. Our goal with this study was to review the number of pediatric patients with granulomatous cervical adenitis and determine the incidence of identification of a specific organism as both healthcare providers and parents are interested in identifying the causative pathogen. Methods: A retrospective chart review was conducted of patients at a high-volume tertiary care children's hospital between 2017 and 2023. Children were included if they underwent a surgical procedure for lymphadenopathy. Pathology, microbiology, and other laboratory reports were reviewed to document the presence of granulomatous cervical adenitis and the incidence of identification of a specific organism. Additional data collected included patient demographics and type of procedure. Results: Of the 1538 charts reviewed, 163 patients underwent an inclusionary procedure. Mean patient age was 10.7 years (range 2.4 months-20 years), 70 (43%) were female, 25 (15%) had granulomatous cervical adenitis, and a specific organism was identified in 9 of these. Conclusion: Despite the availability of a number of ancillary tests, our data demonstrate that the identification of a specific pathogen in cases of granulomatous cervical lymphadenitis is rare. As a result, physicians should be prepared to rely primarily on the history and physical exam findings to determine a working diagnosis as well as a medical and/or surgical treatment plan.

Treating Anxiety-Based Cognitive Distortions Pertaining to Somatic Perception for Better Chronic Pain Outcomes: A Recommendation for Better Practice in the Present Day and the Cyber Age of Medicine.

Anxiety-based cognitive distortions pertaining to somatic perception (ABCD-SPs)-primarily catastrophizing, fear avoidance, and kinesiophobia-have been repeatedly linked to worsening chronic, non-cancer pain (CNCP) outcomes of increased disability, amplified pain, ineffective opioid use, and opioid misuse. Several studies have suggested that treating ABCD-SPs can improve pain outcomes, yet identification and targeting of ABCD-SPs are not part of the standard medical pain assessment and treatment plan. Utilizing a narrative review of proposed mechanisms, published patient perspectives, and study correlations connecting these cognitive distortions with CNCP outcomes, an approach for better practice in the delivery of standard medical CNCP care can be deduced and formulated into a Belief and Behavior Action Plan (BBAP) for medical clinicians treating CNCP to implement into initial and maintenance care planning. These recommendations require relatively few resources to implement and have the potential to disseminate more effective CNCP treatment on a large scale now and in the future with the new frontier of cognitive computing in medicine.

Investigating the pathophysiology and evolution of internal carotid dissection: a fluid-structure interaction simulation study.

Arterial dissection, a condition marked by the tearing of the carotid artery's inner layers, can result in varied clinical outcomes, including progression, stability, or spontaneous regression. Understanding these outcomes' underlying mechanisms is crucial for enhancing patient care, particularly with the increasing use of computer simulations in medical diagnostics and treatment planning. The aim of this study is to utilize computational analysis of blood flow and vascular wall to: (1) understand the pathophysiology of stroke-like episodes in patients with carotid artery dissection; and (2) assess the effectiveness of this method in predicting the evolution of carotid dissection. Utilizing contrast-enhanced magnetic resonance angiography (MRA), we segmented images of the patient's right internal carotid artery. These images were transformed into 3D solids for simulation in Ansys multifisic software, employing a two-way fluid structure interaction (FSI) analysis. Simulations were conducted across two wall conditions (atherosclerotic and normal) and three pressure states (hypotension, normotension, hypertension). The simulations indicated a significant pressure discrepancy between the true and false lumens of the artery. This suggests that flap motion and functional occlusion under hypertensive conditions could be the cause of the clinical episodes. Thrombotic risk and potential for dissection extension were not found to be critical concerns. However, a non-negligible risk of vessel dilation was assessed, aligning with the patient's clinical follow-up data. This study highlights specific hemodynamic parameters that could elucidate carotid artery dissection's mechanisms, offering a potential predictive tool for assessing dissection progression and informing personalized patient care strategies.

Epidemiology and real-world management of childhood epilepsy in Japan: A descriptive study using a health claim database

IntroductionThe purpose of this epidemiological study was to assess the prevalence, comorbidities, and real-world management of childhood epilepsy to provide insights for enhancing epilepsy management and medical resource planning. Materials and methodsThe study encompassed insured individuals aged 0–17 years as of December 2018 who were registered at any point in 2018, for at least part of the year, in a Japanese health claims database spanning January—December 2018. Epilepsy was defined as a diagnosis of epilepsy based on the International Classification of Diseases, 10th Revision codes, and a claimed management fee for epilepsy or an anti-seizure medication (ASM) prescription for longer than 4 weeks. The prevalence of epilepsy, patient characteristics, including comorbidities, and management status, such as prescription of ASMs, were evaluated. ResultsAmong 1528,905 registered children, 9279 were identified as having epilepsy. The prevalence of epilepsy was the lowest at 1.97 per 1000 population (95 % confidence interval [CI] 1.80–2.15) in the 0–2-year age group and increased with age to 9.34 per 1000 population (95 % CI 8.98–9.72) in the 15–17-year age group, with a significantly higher prevalence in boys than in girls in the ≥12-year age group. ASMs were prescribed to 88.3 %–91.9 % of the patients. Moreover, 27 (0.29 %) patients underwent epilepsy surgery. The frequency of claiming intravenous ASMs and long-term electroencephalogram fees increased with a decrease in age. ConclusionsOur findings indicate that young children receive more medical resources than adolescents and that epilepsy surgery is underutilized. Further investigations will help improve the management of and develop measures against epilepsy.

Learning with Explicit Shape Priors for Medical Image Segmentation.

Medical image segmentation is a fundamental task for medical image analysis and surgical planning. In recent years, UNet-based networks have prevailed in the field of medical image segmentation. However, convolutional neural networks (CNNs) suffer from limited receptive fields, which fail to model the long-range dependency of organs or tumors. Besides, these models are heavily dependent on the training of the final segmentation head. And existing methods can not well address aforementioned limitations simultaneously. Hence, in our work, we proposed a novel shape prior module (SPM), which can explicitly introduce shape priors to promote the segmentation performance of UNet-based models. The explicit shape priors consist of global and local shape priors. The former with coarse shape representations provides networks with capabilities to model global contexts. The latter with finer shape information serves as additional guidance to relieve the heavy dependence on the learnable prototype in the segmentation head. To evaluate the effectiveness of SPM, we conduct experiments on three challenging public datasets. And our proposed model achieves state-of-the-art performance. Furthermore, SPM can serve as a plug-and-play structure into classic CNNs and Transformer-based backbones, facilitating the segmentation task on different datasets. Source codes are available at https://github. com/AlexYouXin/Explicit-Shape-Priors.

RDAG U-Net: An Advanced AI Model for Efficient and Accurate CT Scan Analysis of SARS-CoV-2 Pneumonia Lesions.

Background/Objective: This study aims to utilize advanced artificial intelligence (AI) image recog-nition technologies to establish a robust system for identifying features in lung computed tomog-raphy (CT) scans, thereby detecting respiratory infections such as SARS-CoV-2 pneumonia. Spe-cifically, the research focuses on developing a new model called Residual-Dense-Attention Gates U-Net (RDAG U-Net) to improve accuracy and efficiency in identification. Methods: This study employed Attention U-Net, Attention Res U-Net, and the newly developed RDAG U-Net model. RDAG U-Net extends the U-Net architecture by incorporating ResBlock and DenseBlock modules in the encoder to retain training parameters and reduce computation time. The training dataset in-cludes 3,520 CT scans from an open database, augmented to 10,560 samples through data en-hancement techniques. The research also focused on optimizing convolutional architectures, image preprocessing, interpolation methods, data management, and extensive fine-tuning of training parameters and neural network modules. Result: The RDAG U-Net model achieved an outstanding accuracy of 93.29% in identifying pulmonary lesions, with a 45% reduction in computation time compared to other models. The study demonstrated that RDAG U-Net performed stably during training and exhibited good generalization capability by evaluating loss values, model-predicted lesion annotations, and validation-epoch curves. Furthermore, using ITK-Snap to convert 2D pre-dictions into 3D lung and lesion segmentation models, the results delineated lesion contours, en-hancing interpretability. Conclusion: The RDAG U-Net model showed significant improvements in accuracy and efficiency in the analysis of CT images for SARS-CoV-2 pneumonia, achieving a 93.29% recognition accuracy and reducing computation time by 45% compared to other models. These results indicate the potential of the RDAG U-Net model in clinical applications, as it can accelerate the detection of pulmonary lesions and effectively enhance diagnostic accuracy. Additionally, the 2D and 3D visualization results allow physicians to understand lesions' morphology and distribution better, strengthening decision support capabilities and providing valuable medical diagnosis and treatment planning tools.

B - 8 Preemie-Pediatric Left Middle Cerebral Arterial Cerebrovascular Accident and Comorbid Focal Left Central Epilepsy: a Case Study

Abstract Objective Literature regarding neurocognitive functioning in pediatric middle cerebral arterial (MCA) is limited. The aim of this study was to enhance the field’s knowledge and understanding of the presentation and impact of preemie-pediatric left MCA in utero with comorbid left-brain epilepsy in overall neurocognitive and behavioral functioning. Method The subject is a 12-year-old left-handed male with history of previously described events. The purpose of this evaluation was to determine current levels of neurocognitive and behavioral functioning as it relates to his medical history, treatment planning, and prognosis. Data collection was part of a pre-surgical consult to reduce epileptic activity. Results Results suggest overall cognitive functioning fell within the average range. However, relative difficulties in areas of attention and executive functioning were indicated. While all other domains were grossly intact, his profile indicates strengths in visual compared to verbal memory. Interestingly, the patient performed better on verbal tasks after a delay both with and without cuing. This suggests the presentation of a unique trait of pediatric epilepsy in that due to slowed processing speed as seen on the WISC, this patient is likely to remember more information over time. His academic achievements also fell within age-expectations. Conclusions This study impresses the importance of prompt neuropsychological evaluation in providing insight to the patient’s impairments and contribute to adaptive neurocognitive functioning moving forward. While idiopathic, this case demonstrates the need for continued research in pediatric-infantile infarct and provides the opportunity to improve the field’s understanding of the interpretation of similar cases.

The selection of hospital sites and adaptive medical resource planning in the post-COVID era: navigating public health crises in China?

Urban management and planning during public health emergencies, such as the COVID-19 pandemic, are critical. This article examines hospital site selection in Yinzhou, Ningbo, China, using geographical data analysis. It evaluates travel costs and referral patterns between hospitals and community health facilities to establish a sustainable hierarchical diagnosis and treatment system. The results reveal that existing medical facilities, particularly in rural areas, are inadequate during public health emergencies. The article proposes considering total transportation costs in allocating the sites of new hospitals and suggests adopting a ‘permanent + temporary’ facility planning approach to ease financial burdens. Addressing these issues can mitigate the inequitable distribution of urban and rural medical resources. This article was published open access under a CC BY licence: https://creativecommons.org/licenses/by/4.0/ .

How to establish a new medical school? A scoping review of the key considerations.

Establishing new medical schools in medically under-served regions is suggested as part of the solution to the problem of doctor shortages and maldistributions. Establishing a new medical school is, however, a complex undertaking with high financial and political stakes. Critically, the evidence-base for this significant activity has not previously been elucidated. This paper presents the first scoping review on this vitally important, yet under-researched aspect of medical education and health workforce planning. To better understand the process of new medical school establishment, this review posed two research questions: (1) What is the nature of the available literature on establishing a new medical school?; (2) What are the key factors to be considered when establishing a new medical school? Five databases and grey literature were searched in 2015 and 2021 for English-language articles, using search terms related to new medical schools and their establishment. Inclusion and exclusion criteria were based on relevance and suitability in answering the research questions. Seventy-eight articles were analysed both structurally and thematically to understand the nature of the literature and the key considerations involved. Structurally, most articles were descriptive pieces outlining personal and institutional experiences and did not make use of research methodologies nor theory. Thematically, thirteen key considerations were identified including reasons for establishment; location choices; leadership and governance; costs and funding; partnerships; staffing; student numbers; student recruitment; curriculum design and implementation; clinical training sites; buildings and facilities; information and technology resources; and accreditation. Significant gaps in the literature included how to obtain the initial permission from governing authorities and the personal costs and burnout experienced by founding leaders and staff. Although, the literature on new medical school establishment is empirically and theoretically under-developed, it is still useful and reveals a number of important considerations that could assist founding leaders and teams to maximise the outcomes and impact of their establishment efforts. Critically, the evidence-base underpinning this complex undertaking needs to be better informed by theory and research.

A systematic review of indications when and how a military walking blood bank could bridge blood product unavailability.

Blood supply problems in remote areas are well known. To overcome this shortage, many countries have developed innovative walking blood bank (WBB) protocols. However, no common standards have yet been set for their use and common actions. Given that these procedures involve a certain risk, it would be interesting to analyse the activating criteria that lead to using this unusual protocol. Thus, this review aimed to identify indications for a WBB and the common risk mitigation measures. This PRISMA-compliant review only included studies published from 1985 to 25th of January 2023 that describe adult male military casualties requiring blood transfused locally using a walking blood transfusion protocol. All relevant data (i.e., activation and contextual factors and risk mitigation measures) were tabulated to retrieve information from the selected military studies. Our results indicated that activation criteria were homogeneous across the 12 reviewed studies. Whole blood was collected from a WBB when there was a shortage of blood products and when platelets were needed. In the literature reviewed, the main risks associated with such a protocol, namely hemolytic adverse events and transfusion transmitted diseases, are mitigated by the use of typing and screening measures if they are reported. However, there is less consistency in the implementation of those risk mitigation measures. This unusual protocol needs to be integrated into the medical support plan until conventional transfusion support can take over, and should include on-site blood collection from a donor, whether a WBB or an emergency donor panel. The benefits of such a protocol outweigh the risks in a life-threatening situation, especially since these risks can be anticipated and minimised by planning to pre-screen all potential donors before their deployment. Finally, educating and training the staff who must implement this unusual procedure can also improve the safety and survival rate of future patients.

Classification and segmentation of kidney MRI images for chronic kidney disease detection

Chronic Kidney Disease (CKD) is a common ailment with significant public health implications, underscoring the critical importance of early detection and diagnosis for effective management. Our research focuses on utilizing deep learning models to accurately classify and segment kidney MRI images, aiding in the timely detection and diagnosis of CKD. Utilizing the Zenodo dataset comprising 1299 kidney MRI images from 100 T2-weighted abdomen MRI scans, our investigation reveals that Vision Transformers (ViT) based model EfficientNet_b1 exhibited superior performance compared to other models. It achieved an accuracy of 94.38% in distinguishing between healthy and CKD cases in kidney MRI image classification. Moreover, the pretrained models Densenet201 and VGG19, as well as the ViT-based models EfficientNet_b1 and tf_mobilenetv3_large_075, demonstrate remarkable sensitivity and accuracy while compared to identifying kidney MRI images which highlights the effectiveness of deep learning architectures in identifying diseases. Additionally, our segmentation analysis reveals that our proposed network, Resnet18-Self-ONN-UNet++, adeptly delineates kidney structures. The network shows remarkable performance in medical diagnosis and treatment planning, with an Intersection over Union (IoU) of 82.34% and DS of 91.57% when integrated with the Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique and Simultaneous truth and performance level estimation (STAPLE) as post processing technique. In summary, deep learning-driven kidney MRI image classification and segmentation enhance medical imaging analysis, potentially aiding in early CKD diagnosis and treatment, thereby addressing critical public health concerns, and enhancing patient outcomes.