Explore the vast range of titles available.

\"Combining the best current medical knowledge with a new approach grounded in integrative medicine, Chopra and Tanzi offer a groundbreaking new model of healing and the healing system, one of the main mysteries in the mind-body connection\"-- Provided by publisher.

\"Problem-based learning (PBL) has excited interest among educators around the world for several decades. Among the most notable applications of PBL is the approach taken at the Faculty of Health, Medicine and Life Sciences (FHML) at Maastricht University, the Netherlands. Starting in 1974 as a medical school, the faculty embarked on the innovative pathway of problem-based learning, trying to establish a medical training program which applied recent insights of education which would be better adapted to the needs of the modem physician. The medical school, currently part of the FHML, can be considered as an 'established' school, where original innovations and educational changes have become part of a routine. The first book to bring this wealth of information together, \"Lessons from Problem-based Learning\" documents those findings and shares the experiences of those involved, to encourage further debate and refinement of problem-based learning in specific applications elsewhere and in general educational discussion and thought. Each chapter provides a description of why and what has been done in the Maastricht program, followed by reflection on the benefits and issues that have arisen for these developments. The final section of the book examines the application of PBL in the future, and how it is likely to develop further\"--Provided by publisher.

In biomedical research, it is often desirable to seek consensus among individuals who have differing perspectives and experience. This is important when evidence is emerging, inconsistent, limited, or absent. Even when research evidence is abundant, clinical recommendations, policy decisions, and priority-setting may still require agreement from multiple, sometimes ideologically opposed parties. Despite their prominence and influence on key decisions, consensus methods are often poorly reported. Our aim was to develop the first reporting guideline dedicated to and applicable to all consensus methods used in biomedical research regardless of the objective of the consensus process, called ACCORD (ACcurate COnsensus Reporting Document). We followed methodology recommended by the EQUATOR Network for the development of reporting guidelines: a systematic review was followed by a Delphi process and meetings to finalize the ACCORD checklist. The preliminary checklist was drawn from the systematic review of existing literature on the quality of reporting of consensus methods and suggestions from the Steering Committee. A Delphi panel (n = 72) was recruited with representation from 6 continents and a broad range of experience, including clinical, research, policy, and patient perspectives. The 3 rounds of the Delphi process were completed by 58, 54, and 51 panelists. The preliminary checklist of 56 items was refined to a final checklist of 35 items relating to the article title (n = 1), introduction (n = 3), methods (n = 21), results (n = 5), discussion (n = 2), and other information (n = 3). The ACCORD checklist is the first reporting guideline applicable to all consensus-based studies. It will support authors in writing accurate, detailed manuscripts, thereby improving the completeness and transparency of reporting and providing readers with clarity regarding the methods used to reach agreement. Furthermore, the checklist will make the rigor of the consensus methods used to guide the recommendations clear for readers. Reporting consensus studies with greater clarity and transparency may enhance trust in the recommendations made by consensus panels.

\"Structures of Indifference examines an Indigenous life and death in a Canadian city, and what it reveals about the ongoing history of colonialism. At the heart of this story is a thirty-four-hour period in September 2008. During that day and half, Brian Sinclair, a middle-aged, non-Status Anishinaabeg resident of Manitoba's capital city, arrived in the emergency room of the Health Sciences Centre, Winnipeg's major downtown hospital, was left untreated and unattended to, and ultimately died from an easily treatable infection. His death reflects a particular structure of indifference born of and maintained by colonialism. McCallum and Perry present the ways in which Sinclair, once erased and ignored, came to represent diffuse, yet singular and largely dehumanized ideas about Indigenous people, modernity, and decline in cities. This story tells us about ordinary indigeneity in the City of Winnipeg through Sinclair's experience and restores the complex humanity denied him in his interactions with Canadian health and legal systems, both before and after his death. Structures of Indifference completes the story left untold by the inquiry into Sinclair's death, the 2014 report of which omitted any consideration of underlying factors, including racism and systemic discrimination.\"--Provided by publisher.

There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task. A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong's test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855-0.866) on the joint MSH-NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU (P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both <0.001). The highest internal performance was achieved by combining training and test data from MSH and NIH (AUC 0.931, 95% CI 0.927-0.936), but this model demonstrated significantly lower external performance at IU (AUC 0.815, 95% CI 0.745-0.885, P = 0.001). To test the effect of pooling data from sites with disparate pneumonia prevalence, we used stratified subsampling to generate MSH-NIH cohorts that only differed in disease prevalence between training data sites. When both training data sites had the same pneumonia prevalence, the model performed consistently on external IU data (P = 0.88). When a 10-fold difference in pneumonia rate was introduced between sites, internal test performance improved compared to the balanced model (10× MSH risk P < 0.001; 10× NIH P = 0.002), but this outperformance failed to generalize to IU (MSH 10× P < 0.001; NIH 10× P = 0.027). CNNs were able to directly detect hospital system of a radiograph for 99.95% NIH (22,050/22,062) and 99.98% MSH (8,386/8,388) radiographs. The primary limitation of our approach and the available public data is that we cannot fully assess what other factors might be contributing to hospital system-specific biases. Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions.

While it is recognized that language can pose a barrier to the transfer of scientific knowledge, the convergence on English as the global language of science may suggest that this problem has been resolved. However, our survey searching Google Scholar in 16 languages revealed that 35.6% of 75,513 scientific documents on biodiversity conservation published in 2014 were not in English. Ignoring such non-English knowledge can cause biases in our understanding of study systems. Furthermore, as publication in English has become prevalent, scientific knowledge is often unavailable in local languages. This hinders its use by field practitioners and policy makers for local environmental issues; 54% of protected area directors in Spain identified languages as a barrier. We urge scientific communities to make a more concerted effort to tackle this problem and propose potential approaches both for compiling non-English scientific knowledge effectively and for enhancing the multilingualization of new and existing knowledge available only in English for the users of such knowledge.

First are cases in which the data sources themselves do not reflect true epidemiology within a given demographic, as for instance in population data biased by the entrenched overdiagnosis of schizophrenia in African Americans [8]. [...]are cases in which an algorithm is trained on a data set that does not contain enough members of a given demographic—for instance, an algorithm trained mostly on data from older white men. [...]the disclosure of basic yet meaningful details about medical treatment to patients—a fundamental tenet of medical ethics—requires that the doctors themselves grasp at least the fundamental inner workings of the devices they use. [...]for MLm to be ethical, developers must communicate to their end users—doctors—the general logic behind MLm-based decisions. [...]the allocation and grounds for liability for adverse events related to the use of MLm will need to be clarified.

Following a proposal made by the French and German Ministers for Foreign Affairs at the November 2020 Paris Peace Forum, 4 global partners, the Food and Agriculture Organization (FAO), the World Organization for Animal Health (OIE), the United Nations Environment Programme (UNEP), and the World Health Organization (WHO), in May 2021 established the interdisciplinary One Health High-Level Expert Panel (OHHLEP) (https://www.who.int/groups/one-health-high-level-expert-panel) to enhance their cross-sectoral collaboration. There is no shortage of “One Health” definitions in the published literature and among institutions and organizations. [...]an immediate priority for OHHLEP was to develop consensus around a working definition as a solid basis to support a common understanding among the panel members and the partner organizations. Key underlying principles including 1. equity between sectors and disciplines; 2. sociopolitical and multicultural parity (the doctrine that all people are equal and deserve equal rights and opportunities) and inclusion and engagement of communities and marginalized voices; 3. socioecological equilibrium that seeks a harmonious balance between human–animal–environment interaction and acknowledging the importance of biodiversity, access to sufficient natural space and resources, and the intrinsic value of all living things within the ecosystem; 4. stewardship and the responsibility of humans to change behavior and adopt sustainable solutions that recognize the importance of animal welfare and the integrity of the whole ecosystem, thus securing the well-being of current and future generations; and 5. transdisciplinarity and multisectoral collaboration, which includes all relevant disciplines, both modern and traditional forms of knowledge and a broad representative array of perspectives. PLoS Pathog 18(6): e1010537. https://doi.org/10.1371/journal.ppat.1010537 About the Authors: One Health High-Level Expert Panel (OHHLEP) Wiku B. Adisasmito Affiliation: Universitas Indonesia, Depok, West Java, Indonesia Salama Almuhairi Affiliation: National Emergency Crisis and Disasters Management Authority, Abu Dhabi, United Arab Emirates Casey Barton Behravesh Affiliation: Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America Pépé Bilivogui Affiliation: World Health Organization, Guinea Country Office, Conakry, Guinea Salome A. Bukachi Affiliation: Institute of Anthropology, Gender and African Studies, University of Nairobi, Nairobi, Kenya Natalia Casas Affiliation: National Ministry of Health, Autonomous City of Buenos Aires, Argentina Natalia Cediel Becerra Affiliation: School of Agricultural Sciences, Universidad de La Salle, Bogotá, Colombia Dominique F. Charron Affiliation: International Development Research Centre, Ottawa, Canada Abhishek Chaudhary Affiliation: Indian Institute of Technology (IIT), Kanpur, India Janice R. Ciacci Zanella Affiliation: Brazilian Agricultural Research Corporation (Embrapa), Embrapa Swine and Poultry, Concórdia, Santa Catarina, Brazil Andrew A. Cunningham Affiliation: Institute of Zoology, Zoological Society of London, London, United Kingdom Osman Dar Affiliations Global Operations Division, United Kingdom Health Security Agency, London, United Kingdom, Global Health Programme, Chatham House, Royal Institute of International Affairs, London, United Kingdom Nitish Debnath Affiliation: Fleming Fund Country Grant to Bangladesh, DAI Global, Dhaka, Bangladesh Baptiste Dungu Affiliations Afrivet B M, Pretoria, South Africa, Faculty of Veterinary Science, University of Kinshasa, Kinshasa, Democratic Republic Congo Elmoubasher Farag Affiliation: Ministry of Public Health, Health Protection & Communicable Diseases Division, Doha, Qatar George F. Gao Affiliation: Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of China David T. S. Hayman Affiliation: Molecular Epidemiology and Public Health Laboratory, Massey University, Palmerston North, New Zealand Margaret Khaitsa Affiliation: Mississippi State University, Starkville, Mississippi,

Long-COVID refers to a variety of symptoms affecting different organs reported by people following Coronavirus Disease 2019 (COVID-19) infection. To date, there have been no robust estimates of the incidence and co-occurrence of long-COVID features, their relationship to age, sex, or severity of infection, and the extent to which they are specific to COVID-19. The aim of this study is to address these issues. We conducted a retrospective cohort study based on linked electronic health records (EHRs) data from 81 million patients including 273,618 COVID-19 survivors. The incidence and co-occurrence within 6 months and in the 3 to 6 months after COVID-19 diagnosis were calculated for 9 core features of long-COVID (breathing difficulties/breathlessness, fatigue/malaise, chest/throat pain, headache, abdominal symptoms, myalgia, other pain, cognitive symptoms, and anxiety/depression). Their co-occurrence network was also analyzed. Comparison with a propensity score-matched cohort of patients diagnosed with influenza during the same time period was achieved using Kaplan-Meier analysis and the Cox proportional hazard model. The incidence of atopic dermatitis was used as a negative control. Long-COVID clinical features occurred and co-occurred frequently and showed some specificity to COVID-19, though they were also observed after influenza. Different long-COVID clinical profiles were observed based on demographics and illness severity.