Medical Model Research Articles

Internal and External Validation of Machine Learning Models for Predicting Acute Kidney Injury Following Non-Cardiac Surgery Using Open Datasets.

This study developed and validated a machine learning model to accurately predict acute kidney injury (AKI) after non-cardiac surgery, aiming to improve patient outcomes by assessing its clinical feasibility and generalizability. We conducted a retrospective cohort study using data from 76,032 adults who underwent non-cardiac surgery at a single tertiary medical center between March 2019 and February 2021, and used data from 5512 patients from the VitalDB open dataset for external model validation. The predictive variables for model training consisted of demographic, preoperative laboratory, and intraoperative data, including calculated statistical values such as the minimum, maximum, and mean intraoperative blood pressure. When predicting postoperative AKI, our gradient boosting machine model incorporating all the variables achieved the best results, with AUROC values of 0.868 and 0.757 for the internal and external validations using the VitalDB dataset, respectively. The model using intraoperative data performed best in internal validation, while the model with preoperative data excelled in external validation. In this study, we developed a predictive model for postoperative AKI in adult patients undergoing non-cardiac surgery using preoperative and intraoperative data, and external validation demonstrated the efficacy of open datasets for generalization in medical artificial modeling research.

Radiologist preferences for faculty development initiatives to improve resident feedback in the era of competency-based medical education

ABSTRACT Introduction Since 2022, all Canadian post-graduate medical programs have transitioned to a Competence by Design (CBD) model within a Competency-Based Medical Education (CBME) framework. The CBME model emphasized more frequent, formative assessment of residents to evaluate their progress towards predefined competencies in comparison to traditional medical education models. Faculty members therefore have increased responsibility for providing assessments to residents on a more regular basis, which has associated challenges. Our study explores faculty assessment behaviours within the CBD framework and assesses their openness to opportunities aimed at improving the quality of written feedback. Specifically, we explore faculty’s receptiveness to routine metric performance reports that offer comprehensive feedback on their assessment patterns. Methods Online surveys were distributed to all 28 radiology faculty at Queen’s University. Data were collected on demographics, feedback practices, motivations for improving the teacher-learner feedback exchange, and openness to metric performance reports and quality improvement measures. Following descriptive statistics, unpaired t-tests and one-way analysis of variance were conducted to compare groups based on experience and subspecialty. Results The response rate was 89% (25/28 faculty). 56% of faculty were likely to complete evaluations after working with a resident. Regarding the degree to which faculty felt written feedback is important, 62% found it at least moderately important. A majority (67%) believed that performance reports could influence their evaluation approach, with volume of written feedback being the most likely to change. Faculty expressed interest in feedback-focused development opportunities (67%), favouring Grand Rounds and workshops. Conclusion Assessment of preceptor perceptions reveals that faculty recognize the importance of offering high-quality written feedback to learners. Faculty openness to quality improvement interventions for curricular reform relies on having sufficient time, knowledge, and skills for effective assessments. This suggests that integrating routine performance metrics into faculty assessments could serve as a catalyst for enhancing future feedback quality.

STILL SUFFERING FROM THE ‘SILO EFFECT’: LINGERING CULTURAL BARRIERS TO COLLABORATIVE CARE

This research project sought to draw out the contesting definitions of collaborative care among professional subgroups in maternity services. The paper contrasts medical and social models of knowledge and reports on qualitative evidence from midwives and doctors in Australian hospitals. The evidence indicates that collaborative care is welcomed by both midwives and doctors but that there remains a lingering residue of the ‘silo effect’ of the ‘old’ professionalism, characterized by hierarchical relations, divergent philosophies and competing domains. Although a ‘new professionalism’ has emerged that challenges the old hierarchies and professional dependencies, it too harbours lingering residues of the former dichotomy between midwives and obstetricians. These tensions and enmities will need to be resolved before genuine collaboration may take full effect. The objective is a relationship- focused model of care that transcends professional or woman-focused models. The ‘new’ professionalism may be expedited through mediation strategies, a version of which is the ‘sociological intervention method’ discussed in this article.1

Trajectories of medical care expenditure in the last year of life associated with long-term care utilization in frail older adults: A retrospective cohort study.

Medical care and long-term care utilization in the last year of life of frail older adults could be a key indicator of their quality of life. This study aimed to identify the medical care expenditure (MCE) trajectories in the last year of life of frail older adults by investigating the association between MCE and long-term care utilization in each trajectory. The retrospective cohort study of three municipalities in Japan included 405 decedents (median age at death, 85 years; 189 women [46.7%]) from a cohort of 1,658 frail older adults aged ≥65 years who were newly certified as support level in the long-term care insurance program from April 2012 to March 2013. This study used long-term care and medical insurance claim data from April 2012 to March 2017. The primary outcome was MCE over the 12 months preceding death. Group-based trajectory modeling was conducted to identify the MCE trajectories. A mixed-effect model was employed to examine the association between long-term care utilization and MCE in each trajectory. Participants were stratified into four groups based on MCE trajectories over the 12 months preceding death as follows: rising (n = 159, 39.3%), persistently high (n = 143, 35.3%), minimal (n = 56, 13.8%), and descending (n = 47, 11.6%) groups. Home-based long-term care utilization was associated with increased MCE in the descending trajectory (coefficient, 1.48; 95% confidence interval [CI], 1.35-1.62). Facility-based long-term care utilization was associated with reduced MCE in the rising trajectory (coefficient, 0.59; 95% CI, 0.50-0.69). Both home-based (coefficient, 0.92; 95% CI, 0.85-0.99) and facility-based (coefficient; 0.53; 95% CI, 0.41-0.63) long-term care utilization were associated with reduced MCE in the persistently high trajectory. These findings may facilitate the integration of medical and long-term care models at the end of life in frail older adults.

The development of day surgery in China and the effectiveness and reflection of day surgery in ophthalmology-specialized hospitals.

This survey investigates the development of day surgery in China, and analyzes the national policy support, medical service management model, disease types of day surgery, medical insurance payment methods, and the medical service capacity, efficiency, quality and safety, health economics indicators, and patient satisfaction after the implementation of day surgery in a tertiary eye hospital. After more than 20 years of development, China's day surgery has shown a good development trend. The implementation of day surgery in eye hospitals accounts for more than 70% of elective surgery, and patients, medical institutions, and medical insurance institutions have all achieved good social benefits.

Assessment of the accuracy of 3D printed medical models through reverse engineering

The dimensional accuracy of additively manufactured (3D printed) medical models can be affected by various parameters. Although different methods are used to evaluate the accuracy of additively manufactured models, this study focused on the investigation of the dimensional accuracy of the medical model based the combination of reverse engineering (RE) and additive manufacturing (AM) technologies. Human femur bone was constructed from CT images and manufactured, using Fortus 450mc Industrial material extrusion 3D Printer. The additive manufactured femur bone was subsequently 3D scanned using three distinct non-contact 3D scanners. MeshLab was used for mesh analysis, while VX Elements was used for post-processing of the point cloud. A combination of the VX Inspect environment and MeshLab was used to evaluate the scanning performance. The deviation of the 3D scanned 3D models from the reference mesh was determined using relative metrics and absolute measurements. The scanners reported deviations ranging from −0.375 mm to 0.388 mm, resulting in a total range of approximately 0.763 mm with average root mean square (RMS) deviation of 0.22 mm. The results indicate that the additively manufactured model, as measured by 3D scanning, has a mean deviation with an average range of approximately 0.46 mm and an average mean value of around 0.16 mm.

Assessing and Optimizing Large Language Models on Spondyloarthritis Multi-Choice Question Answering: Protocol for Enhancement and Assessment.

Spondyloarthritis (SpA), a chronic inflammatory disorder, predominantly impacts the sacroiliac joints and spine, significantly escalating the risk of disability. SpA's complexity, as evidenced by its diverse clinical presentations and symptoms that often mimic other diseases, presents substantial challenges in its accurate diagnosis and differentiation. This complexity becomes even more pronounced in nonspecialist health care environments due to limited resources, resulting in delayed referrals, increased misdiagnosis rates, and exacerbated disability outcomes for patients with SpA. The emergence of large language models (LLMs) in medical diagnostics introduces a revolutionary potential to overcome these diagnostic hurdles. Despite recent advancements in artificial intelligence and LLMs demonstrating effectiveness in diagnosing and treating various diseases, their application in SpA remains underdeveloped. Currently, there is a notable absence of SpA-specific LLMs and an established benchmark for assessing the performance of such models in this particular field. Our objective is to develop a foundational medical model, creating a comprehensive evaluation benchmark tailored to the essential medical knowledge of SpA and its unique diagnostic and treatment protocols. The model, post-pretraining, will be subject to further enhancement through supervised fine-tuning. It is projected to significantly aid physicians in SpA diagnosis and treatment, especially in settings with limited access to specialized care. Furthermore, this initiative is poised to promote early and accurate SpA detection at the primary care level, thereby diminishing the risks associated with delayed or incorrect diagnoses. A rigorous benchmark, comprising 222 meticulously formulated multiple-choice questions on SpA, will be established and developed. These questions will be extensively revised to ensure their suitability for accurately evaluating LLMs' performance in real-world diagnostic and therapeutic scenarios. Our methodology involves selecting and refining top foundational models using public data sets. The best-performing model in our benchmark will undergo further training. Subsequently, more than 80,000 real-world inpatient and outpatient cases from hospitals will enhance LLM training, incorporating techniques such as supervised fine-tuning and low-rank adaptation. We will rigorously assess the models' generated responses for accuracy and evaluate their reasoning processes using the metrics of fluency, relevance, completeness, and medical proficiency. Development of the model is progressing, with significant enhancements anticipated by early 2024. The benchmark, along with the results of evaluations, is expected to be released in the second quarter of 2024. Our trained model aims to capitalize on the capabilities of LLMs in analyzing complex clinical data, thereby enabling precise detection, diagnosis, and treatment of SpA. This innovation is anticipated to play a vital role in diminishing the disabilities arising from delayed or incorrect SpA diagnoses. By promoting this model across diverse health care settings, we anticipate a significant improvement in SpA management, culminating in enhanced patient outcomes and a reduced overall burden of the disease. DERR1-10.2196/57001.

Impact of high-quality, mixed-domain data on the performance of medical language models.

To optimize the training strategy of large language models for medical applications, focusing on creating clinically relevant systems that efficiently integrate into healthcare settings, while ensuring high standards of accuracy and reliability. We curated a comprehensive collection of high-quality, domain-specific data and used it to train several models, each with different subsets of this data. These models were rigorously evaluated against standard medical benchmarks, such as the USMLE, to measure their performance. Furthermore, for a thorough effectiveness assessment, they were compared with other state-of-the-art medical models of comparable size. The models trained with a mix of high-quality, domain-specific, and general data showed superior performance over those trained on larger, less clinically relevant datasets (P < .001). Our 7-billion-parameter model Med5 scores 60.5% on MedQA, outperforming the previous best of 49.3% from comparable models, and becomes the first of its size to achieve a passing score on the USMLE. Additionally, this model retained its proficiency in general domain tasks, comparable to state-of-the-art general domain models of similar size. Our findings underscore the importance of integrating high-quality, domain-specific data in training large language models for medical purposes. The balanced approach between specialized and general data significantly enhances the model's clinical relevance and performance. This study sets a new standard in medical language models, proving that a strategically trained, smaller model can outperform larger ones in clinical relevance and general proficiency, highlighting the importance of data quality and expert curation in generative artificial intelligence for healthcare applications.

Frailty and risk of systemic atherosclerosis: A bidirectional Mendelian randomization study.

Numerous observational studies have reported an association between frailty and atherosclerosis. However, the causal relationship between frailty and the occurrence of atherosclerosis in different anatomical sites remains unclear. we conducted a bidirectional Mendelian randomization (MR) study to evaluate the causal relationship between the frailty index (FI), and both systemic atherosclerosis and lipids. We obtained summary statistics from large-scale genome-wide association studies (GWAS) of various phenotypes, including frailty (n = 175,226), coronary atherosclerosis (n = 56,685), cerebral atherosclerosis (n = 150,765), peripheral arterial disease (PAD) (n = 361,194), atherosclerosis at other sites (n = 17,832), LDL-C (n = 201,678), HDL-C (n = 77,409), and triglycerides (n = 78,700). The primary MR analysis employed the inverse variance weighted (IVW) method. Furthermore, to assess reverse causality, we employed inverse MR and multivariate MR analysis. Genetically predicted FI showed positive associations with the risk of coronary atherosclerosis (OR = 1.47, 95% CI 1.12-1.93) and cerebral atherosclerosis (OR = 1.99, 95% CI 1.05-3.78), with no significant association (p >0.05) applied to peripheral arterial disease and atherosclerosis at other sites. Genetically predicted FI was positively associated with the risk of triglycerides (OR = 1.31, 95% CI 1.08-1.59), negatively associated with the risk of LDL-C (OR = 0.87, 95% CI 0.78-0.97), and showed no significant association with the risk of HDL-C (p >0.05). Furthermore, both reverse MR and multivariate MR analyses demonstrated a correlation between systemic atherosclerosis, lipids, and increased FI. Our study elucidated that genetically predicted FI is associated with the risk of coronary atherosclerosis and cerebral atherosclerosis by the MR analysis method, and they have a bidirectional causal relationship. Moreover, genetically predicted FI was causally associated with triglyceride and LDL-C levels. Further understanding of this association is crucial for optimizing medical practice and care models specifically tailored to frail populations.

HIV/AIDS, Disability, and Viral Masculinities

AbstractThis article explores the relationship between HIV/AIDS, disability, and masculinity, critically analysing how these elements intersect and influence societal norms and individual identities. It begins by examining the categorisation of HIV/AIDS as a disability, questioning the adequacy of traditional disability frameworks in capturing the complex realities of living with HIV/AIDS. The discussion extends to the implications of such categorisation, considering both the medical and social models of disability, and how these models interact with legal protections and social stigmas. The article also discusses how HIV/AIDS as disability impacts conceptions of masculinity, with a specific focus on how practices like bugchasing subvert traditional notions of health, risk, and masculinity, examining cultural constructions of masculinity in relation to HIV/AIDS and highlighting how societal expectations and popular culture representations often perpetuate certain masculine ideals while marginalising others. By examining these intersections, the article challenges established health paradigms and societal norms around disability, HIV/AIDS, and masculinity. Ultimately, it aims to broaden the dialogue around health, disability, and gender, advocating for policies that reflect the nuanced realities of people living with HIV/AIDS and encouraging a reassessment of entrenched perceptions.

EXPANDING THE ROLE OF EDUCATION IN EATING DISORDER PREVENTION: THE ROLE OF A SOCIO-CULTURAL APPROACH

The research on eating disorder (ED) prevention, evolving since the mid-1990s has primarily focused on medical and psychiatric perspectives, emphasising diagnosis and treatment. At the same, there is a growing recognition that prevention efforts, despite their critical importance, are underrepresented in academic literature. This study aims to promote a more holistic approach, integrating education as a pivotal socio-cultural factor in ED prevention. The study delineates a shift from a predominantly medical model to a more inclusive socio-cultural framework. It stresses the need for interdisciplinary collaboration and emphasises the critical role of educational strategies in the broader context of ED prevention. The aim of this article is to conduct a review of the scientific literature in order to provide a clearer picture of general research trends and paradigms in ED, and to expand the role and relevance of ED prevention in an educational context. While acknowledging that social environments including micro, meso, macro systems influence the development of ED, this paper focuses on the meso environment, highlighting the importance of schools and school professionals in preventing ED.

The impact of adenotonsillectomy on healthcare utilization in children with obstructive sleep apnea: Experience of an integrated medical model

Background and objectivesStudies suggest that children with obstructive sleep apnea (OSA) have increased healthcare utilization patterns in comparison to matched controls. However, the effect of adenotonsillectomy (AT) on utilization patterns in these children is poorly understood. Additionally, no previous studies have compared the effect of AT on healthcare utilization patterns across different OSA severity groups. The aim of this retrospective cohort study is to assess the effects of surgical treatment on the level of healthcare utilization among children with OSA at a large integrated multicenter healthcare system. MethodsRetrospective analysis was performed of children aged 3–12 diagnosed with OSA via an attended polysomnogram (PSG) between December 2016 and February 2019. Demographic variables including age (at time of PSG), body mass index (BMI), race, and ethnicity were obtained. Variables for healthcare utilization were assessed for 12 months prior to PSG, and for 12 months after PSG (or after AT, delayed for the first 30 days after surgery to account for surgery-related visits). Healthcare utilization variables assessed included the total number of outpatient visits, inpatient, and emergency department (ED) visits, visits involving diagnostic codes associated with upper respiratory infection (URI), otitis media (OM), and allergic rhinitis (AR), prescription data involving intranasal steroids or leukotriene receptor antagonists (LTRA), and communication data such as secure message load and specialty referrals. Repeated measure linear difference-in-difference (D-I-D) models were used to assess the causal impact of AT on healthcare utilization outcomes. Sensitivity analyses were performed using modeling with a Poisson distribution and as an unadjusted model, with statistical significance set to p < 0.05. ResultsAnalysis elicited 577 children identified with OSA. Of these, 336 (58.2 %) underwent observation while 241 (41.8 %) underwent AT. The mean age was 6.4 years, with a slight male predominance (60.5 %). Analysis of baseline healthcare utilization patterns revealed that the treatment group had a significantly higher number of baseline inpatient/ED visits and OM visits in comparison to the observation group, but no differences in regards to baseline outpatient visits, or in visits involving URI or AR.Analysis of the entire OSA cohort via D-I-D modelling showed a significantly larger reduction in outpatient visits, secure messages, specialty referrals, and the use of intranasal steroid and LTRA in the treatment group compared to the observation group. Stratification of children based on OSA severity showed that the significant differences in healthcare utilization attributed to surgical treatment were primarily driven by the severe OSA group. Children with severe OSA who underwent AT showed significant reductions in most variables including outpatient visits, inpatient/ED visits, and OM visits. Alternatively, the only significant reductions in healthcare utilization among children with mild OSA treated with AT were in AR visits, intranasal steroid use, and LTRA use. Pattern changes among children with moderate OSA compared similarly to those with mild OSA. ConclusionTo the authors’ knowledge this study represents the largest available study assessing the impact of AT on healthcare utilization in children with OSA that also considers the effect of OSA severity on utilization patterns. AT appears to decrease healthcare utilization patterns, particularly in children with severe OSA. Alternatively, children with mild or moderate OSA treated with AT had only modest reductions in healthcare utilization patterns.

Consolation Effect of Medical Clown Care Model in Children with Inguinal Hernia During Perioperative Period: A Single-Center Study in China

Background: Due to differences in medical backgrounds, there have been few studies on medical clown care in Chinese hospitals. Objectives: This study aimed to explore the consolatory effect of the medical clown care model on children in a Chinese hospital. Methods: This study included 236 children aged 4 - 6 years and divided them into an intervention group (n = 116) and a control group (n = 120). The control group received routine nursing measures, while the intervention group experienced the medical clown care model. To assess the children’s pain levels and the anxiety levels of their parents, the Face Legs Activity Cry and Consolability (FLACC) Scale, Wong-Baker FACES Pain Rating Scale (W-B FPS-R), and Self-Rating Anxiety Scale (SAS) were used. Results: The baseline data for both groups were comparable at admission (P &gt; 0.05). The FLACC score, W-B FPS-R score, and crying rate in the intervention group were significantly lower than those in the control group (4.81 vs 6.71, 4.69 vs 7.02, 44.83 % vs 79.16 %, P &lt; 0.05). Parents in the intervention group also had lower preoperative anxiety scores compared to those in the control group (42.85 vs 52.69, P &lt; 0.05). Furthermore, the postoperative pain incidence and drug use rates were significantly lower in the intervention group (18.97 % vs 30.83 %, 27.27 % vs 56.76 %, P &lt; 0.05). Compliance and satisfaction were significantly higher in the intervention group than in the control group (P &lt; 0.05). Conclusions: The application of the clown care model can effectively reduce children's pain, provide consolation, and relieve parental anxiety.

A Unified Review of Deep Learning for Automated Medical Coding

Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents. Recent advances in deep learning and natural language processing have been widely applied to this task. However, deep learning-based medical coding lacks a unified view of the design of neural network architectures. This review proposes a unified framework to provide a general understanding of the building blocks of medical coding models and summarizes recent advanced models under the proposed framework. Our unified framework decomposes medical coding into four main components, i.e., encoder modules for text feature extraction, mechanisms for building deep encoder architectures, decoder modules for transforming hidden representations into medical codes, and the usage of auxiliary information. Finally, we introduce the benchmarks and real-world usage and discuss key research challenges and future directions.

Exploration of Curriculum Ideology and Politics Integrating Medical Humanities Cultivation Model to Enhance Medical Core Values

Purpose: To explore the application effect of the cultivation mode of Curriculum Ideology and Political Integration of Medical Humanities to enhance the core values of medicine in the clinical internship stage of medical students. Methods: Students in the clinical internship stage of the First Affiliated Hospital of Baotou Medical College were selected, and a total of 156 students in one random class in each grade were taken as the observation group and received the integration cultivation mode; 148 students were taken as the control group and employed the traditional mode. The teaching effect of interns in the two groups was analyzed. Results: The teaching performance of students in both groups after clinical internship teaching was improved compared with that before admission; the teaching performance, teaching effect, teaching evaluation, and teaching satisfaction of the observation group were higher than that of the control group. Conclusion: The integration of the medical humanities training model with curriculum ideology and politics in the clinical internship of medical students is conducive to the improvement of teaching performance, teaching evaluation, teaching satisfaction of teachers and students, and the development and improvement of core values of medical students, which is of good value for teaching application.

Foundational features of indigenous pregnancy care: Lessons learned from indigenous pregnancy care providers

ProblemIndigenous populations experience higher odds of poor maternal and infant health outcomes than non-Hispanic White mothers yet have lower odds of receiving adequate prenatal care. BackgroundMany Indigenous communities rely on modern Western medical institutions to provide pregnancy related health care. These systems were not developed with or for Indigenous communities and often fail to meet the needs of Indigenous pregnant patients. Offering culturally congruent models of care may increase prenatal care utilization. Question, hypothesis or aimThis paper used qualitative inquiry to identify Indigenous approaches to caring for pregnancy. MethodsOur team conducted 16 semi-structured individual interviews and one group interview with a total of 19 respondents. To arrive at thematic categories, the research team engaged in a modified pile sorting technique. The final set of categories, along with sub-themes, descriptions and example quotes, were sent to interviewees for approval. FindingsTen Foundational Features of Indigenous Pregnancy Care were identified. These covered themes related to Indigenous cultural practices, relationships, Indigenous sovereignty, local Indigenous community, full spectrum care, wholistic care, birthing person's wisdom, power and autonomy, flexibility, historical trauma, and cultural awareness. DiscussionModern midwifery care delivered by Indigenous practitioners may partially bridge the cultural gap; however, intentional effort is needed to integrate Indigenous ways into medical doctor practice models and facilities. ConclusionThis paper identifies ten foundational features of Indigenous pregnancy care and demonstrates the importance of recognizing the effects of trauma and providing opportunities for healing, upholding sovereignty, and centering relationships when caring for Indigenous pregnancies.

Semi-Supervised Medical Image Classification with Pseudo Labels Using Coalition Similarity Training

The development of medical image classification models necessitates a substantial number of labeled images for model training. In real-world scenarios, sample sizes are typically limited and labeled samples often constitute only a small portion of the dataset. This paper aims to investigate a collaborative similarity learning strategy that optimizes pseudo-labels to enhance model accuracy and expedite its convergence, known as the joint similarity learning framework. By integrating semantic similarity and instance similarity, the pseudo-labels are mutually refined to ensure their quality during initial training. Furthermore, the similarity score is utilized as a weight to guide samples away from misclassification predictions during the classification process. To enhance the model’s generalization ability, an adaptive consistency constraint is introduced into the loss function to improve performance on untrained datasets. The model achieved a satisfactory accuracy of 93.65% at 80% labeling ratio, comparable to supervised learning methods’ performance. Even with very low labeling ratio (e.g., 5%), the model still attained an accuracy of 74.28%. Comparison with other techniques such as Mean Teacher and FixMatch revealed that our approach significantly outperforms them in medical image classification tasks through improving accuracy by approximately 2%, demonstrating this framework’s leadership in medical image classification.

Europe's Largest Research Infrastructure for Curated Medical Data Models with Semantic Annotations.

Structural metadata from the majority of clinical studies and routine health care systems is currently not yet available to the scientific community. To provide an overview of available contents in the Portal of Medical Data Models (MDM Portal). The MDM Portal is a registered European information infrastructure for research and health care, and its contents are curated and semantically annotated by medical experts. It enables users to search, view, discuss, and download existing medical data models. The most frequent keyword is "clinical trial" (n = 18,777), and the most frequent disease-specific keyword is "breast neoplasms" (n = 1,943). Most data items are available in English (n = 545,749) and German (n = 109,267). Manually curated semantic annotations are available for 805,308 elements (554,352 items, 58,101 item groups, and 192,855 code list items), which were derived from 25,257 data models. In total, 1,609,225 Unified Medical Language System (UMLS) codes have been assigned, with 66,373 unique UMLS codes. To our knowledge, the MDM Portal constitutes Europe's largest collection of medical data models with semantically annotated elements. As such, it can be used to increase compatibility of medical datasets and can be utilized as a large expert-annotated medical text corpus for natural language processing.

Realistic Texture Mapping of 3D Medical Models Using RGBD Camera for Mixed Reality Applications

Augmented and mixed reality in the medical field is becoming increasingly important. The creation and visualization of digital models similar to reality could be a great help to increase the user experience during augmented or mixed reality activities like surgical planning and educational, training and testing phases of medical students. This study introduces a technique for enhancing a 3D digital model reconstructed from cone-beam computed tomography images with its real coloured texture using an Intel D435 RGBD camera. This method is based on iteratively projecting the two models onto a 2D plane, identifying their contours and then minimizing the distance between them. Finally, the coloured digital models were displayed in mixed reality through a Microsoft HoloLens 2 and an application to interact with them using hand gestures was developed. The registration error between the two 3D models evaluated using 30,000 random points indicates values of: 1.1 ± 1.3 mm on the x-axis, 0.7 ± 0.8 mm on the y-axis, and 0.9 ± 1.2 mm on the z-axis. This result was achieved in three iterations, starting from an average registration error on the three axes of 1.4 mm to reach 0.9 mm. The heatmap created to visualize the spatial distribution of the error shows how it is uniformly distributed over the surface of the pointcloud obtained with the RGBD camera, except for some areas of the nose and ears where the registration error tends to increase. The obtained results indicate that the proposed methodology seems effective. In addition, since the used RGBD camera is inexpensive, future approaches based on the simultaneous use of multiple cameras could further improve the results. Finally, the augmented reality visualization of the obtained result is innovative and could provide support in all those cases where the visualization of three-dimensional medical models is necessary.

Eco-anxiety: Towards a medical model and the new framework of ecolalgia.

In the context of global warming, new terms emerged in the global media and in the psychology field to embody the negative feelings which come along with climate change such as 'eco-anxiety' or 'solastalgia'. The pathological character of these emotions is denied although medical opinion is often required for helping people to handle them. Also, no proper medical framework in the field exists to study and care for these patients. In this narrative review, we aim to (1) analyse the concept of eco-anxiety by focusing on its history and developed concepts, (2) summarize the different scales built to assess eco-anxiety and (3) propose a new medical framework. We came out with a framework based on the transformation of a physiological adaptative behaviour the 'eco-distress'. It is composed of three dimensions: eco-anger, eco-grief and eco-worry, it is not debilitating in daily life and promotes coping strategies such as management of negative emotions and pro-environmental behaviours (PEB). It can transform itself into a pathological state, the 'ecolalgia', composed of two core dimensions: eco-anxiety and eco-depression, leading to functional impairment and decrease in PEB. If ecolalgia maintains over 15 days, we propose to consider it as a full psychiatric disorder needing medical advice. This new framework enables a novel approach that is necessary for the improved management of mental health issues related to climate change.