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Theo Vos1, Amanuel Alemu Abajobir, Kalkidan Hassen Abate2, Cristiana Abbafati3 +775 more•Institutions (305)
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016.
10,401 citations
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Tufts Medical Center1, University of Oxford2, Pierre-and-Marie-Curie University3, Erasmus University Rotterdam4, Women's College Hospital5, American Physical Therapy Association6, University of Liège7, Royal North Shore Hospital8, University of Tokyo9, University of Arizona10, Lund University11, Paris Descartes University12, University of Southern Denmark13, Coventry Health Care14
TL;DR: These evidence-based consensus recommendations provide guidance to patients and practitioners on treatments applicable to all individuals with knee OA, as well as therapies that can be considered according to individualized patient needs and preferences.
2,467 citations
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TL;DR: In this article, a total of 2128 calcitic and phosphatic shells, mainly brachiopods with some conodonts and belemnites, were measured for their δ 18 O, δ 13 C and 87 Sr / 86 S values.
2,241 citations
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Radboud University Nijmegen1, Eindhoven University of Technology2, Utrecht University3, Beth Israel Deaconess Medical Center4, Massachusetts Institute of Technology5, Harvard University6, The Chinese University of Hong Kong7, Munich Business School8, Middle East Technical University9, University of Toronto10, University of Warwick11, Coventry Health Care12, Qatar University13, HTW Berlin - University of Applied Sciences14, Tampere University of Technology15, University of Tampere16, Technische Universität München17, Osaka University18, University of South Florida19, National Academy of Sciences of Belarus20, University of Castilla–La Mancha21, Pontifical Catholic University of Peru22, Pierre-and-Marie-Curie University23
TL;DR: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
Abstract: Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884];P Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.
2,116 citations
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TL;DR: Intensified research efforts and global initiatives are clearly needed to address the burden of low back pain as a public health problem, where health and other systems are often fragile and not equipped to cope with this growing burden.
2,114 citations
Authors
Showing all 6585 results
Name | H-index | Papers | Citations |
---|---|---|---|
Salim Yusuf | 231 | 1439 | 252912 |
Gregory Y.H. Lip | 169 | 3159 | 171742 |
Stephen M. Smith | 128 | 501 | 140104 |
Clive Ballard | 117 | 736 | 61663 |
Rachelle Buchbinder | 112 | 613 | 94973 |
Ian M. Adcock | 107 | 660 | 42380 |
Thomas P. Davis | 107 | 724 | 41495 |
Peter J. Sadler | 106 | 719 | 41608 |
Charles Hulme | 90 | 322 | 27332 |
Dieter Wolke | 90 | 444 | 26516 |
Paul J. Thornalley | 89 | 321 | 27613 |
Thomas E. Nichols | 88 | 411 | 58970 |
Keith R. Abrams | 86 | 355 | 30980 |
Alexandra M. Z. Slawin | 85 | 1607 | 38583 |
David A. Leigh | 85 | 339 | 26392 |