Institution
Public Health Research Institute
Healthcare•
About: Public Health Research Institute is a based out in . It is known for research contribution in the topics: Population & Randomized controlled trial. The organization has 4889 authors who have published 8149 publications receiving 276945 citations.
Papers published on a yearly basis
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Istanbul University1, Heidelberg University2, University of Liège3, Karolinska University Hospital4, University of Southampton5, Catholic University of the Sacred Heart6, University of Toulouse7, Newcastle upon Tyne Hospitals NHS Foundation Trust8, University of Erlangen-Nuremberg9, First Faculty of Medicine, Charles University in Prague10, University of Antwerp11, Public Health Research Institute12, University of Verona13
TL;DR: An emphasis is placed on low muscle strength as a key characteristic of sarcopenia, uses detection of low muscle quantity and quality to confirm the sarc Openia diagnosis, and provides clear cut-off points for measurements of variables that identify and characterise sarc openia.
Abstract: Background in 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) published a sarcopenia definition that aimed to foster advances in identifying and caring for people with sarcopenia. In early 2018, the Working Group met again (EWGSOP2) to update the original definition in order to reflect scientific and clinical evidence that has built over the last decade. This paper presents our updated findings. Objectives to increase consistency of research design, clinical diagnoses and ultimately, care for people with sarcopenia. Recommendations sarcopenia is a muscle disease (muscle failure) rooted in adverse muscle changes that accrue across a lifetime; sarcopenia is common among adults of older age but can also occur earlier in life. In this updated consensus paper on sarcopenia, EWGSOP2: (1) focuses on low muscle strength as a key characteristic of sarcopenia, uses detection of low muscle quantity and quality to confirm the sarcopenia diagnosis, and identifies poor physical performance as indicative of severe sarcopenia; (2) updates the clinical algorithm that can be used for sarcopenia case-finding, diagnosis and confirmation, and severity determination and (3) provides clear cut-off points for measurements of variables that identify and characterise sarcopenia. Conclusions EWGSOP2's updated recommendations aim to increase awareness of sarcopenia and its risk. With these new recommendations, EWGSOP2 calls for healthcare professionals who treat patients at risk for sarcopenia to take actions that will promote early detection and treatment. We also encourage more research in the field of sarcopenia in order to prevent or delay adverse health outcomes that incur a heavy burden for patients and healthcare systems.
6,250 citations
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TL;DR: Novel nucleic acid probes that recognize and report the presence of specific nucleic acids in homogeneous solutions that undergo a spontaneous conforma-tional change when they hybridize to their targets are developed.
Abstract: We have developed novel nucleic acid probes that recognize and report the presence of specific nucleic acids in homogeneous solutions. These probes undergo a spontaneous fluorogenic conformational change when they hybridize to their targets. Only perfectly complementary targets elicit this response, as hybridization does not occur when the target contains a mismatched nucleotide or a deletion. The probes are particularly suited for monitoring the synthesis of specific nucleic acids in real time. When used in nucleic acid amplification assays, gene detection is homogeneous and sensitive, and can be carried out in a sealed tube. When introduced into living cells, these probes should enable the origin, movement, and fate of specific mRNAs to be traced.
4,584 citations
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TL;DR: A rapid method has been devised which requires only 5 c.mm.
2,921 citations
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Icahn School of Medicine at Mount Sinai1, Pure Earth2, World Bank3, University of Arizona4, McGill University5, Indian Ministry of Environment and Forests6, Qatar Airways7, University of Health Sciences Antigua8, Ludwig Maximilian University of Munich9, Johns Hopkins University10, Boston College11, Chulabhorn Research Institute12, University of Maryland, College Park13, University of Ghana14, Centro Nacional de Investigaciones Cardiovasculares15, University of Chicago16, University of London17, University of Oxford18, Indian Institute of Technology Delhi19, Simon Fraser University20, Consortium of Universities for Global Health21, University of Ottawa22, Columbia University23, Stockholm Resilience Centre24, Massachusetts Institute of Technology25, University of Queensland26, University of California, Berkeley27, New York University28, National Institutes of Health29, Public Health Research Institute30, United Nations Industrial Development Organization31, Renmin University of China32
TL;DR: This book is dedicated to the memory of those who have served in the armed forces and their families during the conflicts of the twentieth century.
2,628 citations
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Katholieke Universiteit Leuven1, Public Health Research Institute2, Leiden University3, John Radcliffe Hospital4, University of Oxford5, Keele University6, Medical University of Vienna7, University Medical Center Utrecht8, University College Cork9, University of Pennsylvania10, University of Cologne11, Manchester Academic Health Science Centre12, University of Aberdeen13, RMIT University14, University of Manchester15, University of Amsterdam16, University of Ioannina17, Imperial College London18, Maastricht University Medical Centre19, Humboldt University of Berlin20
TL;DR: Proposed models for covid-19 are poorly reported, at high risk of bias, and their reported performance is probably optimistic, according to a review of published and preprint reports.
Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
2,183 citations
Authors
Showing all 4916 results
Name | H-index | Papers | Citations |
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Dorret I. Boomsma | 176 | 1507 | 136353 |
Brenda W.J.H. Penninx | 170 | 1139 | 119082 |
Michael Snyder | 169 | 840 | 130225 |
Lex M. Bouter | 158 | 767 | 103034 |
David Eisenberg | 156 | 697 | 112460 |
Philip Scheltens | 140 | 1175 | 107312 |
Pim Cuijpers | 136 | 982 | 69370 |
Gonneke Willemsen | 129 | 575 | 76976 |
Britton Chance | 128 | 1112 | 76591 |
Coen D.A. Stehouwer | 122 | 970 | 59701 |
Peter J. Anderson | 120 | 966 | 63635 |
Jouke-Jan Hottenga | 120 | 389 | 63039 |
Eco J. C. de Geus | 119 | 522 | 61085 |
Johannes Brug | 109 | 620 | 44832 |
Paul Lips | 109 | 491 | 50403 |