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Institution

Coventry Health Care

About: Coventry Health Care is a based out in . It is known for research contribution in the topics: Population & Mental health. The organization has 6585 authors who have published 6542 publications receiving 146970 citations.


Papers
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
12 Dec 2017-JAMA
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


Authors

Showing all 6585 results

NameH-indexPapersCitations
Salim Yusuf2311439252912
Gregory Y.H. Lip1693159171742
Stephen M. Smith128501140104
Clive Ballard11773661663
Rachelle Buchbinder11261394973
Ian M. Adcock10766042380
Thomas P. Davis10772441495
Peter J. Sadler10671941608
Charles Hulme9032227332
Dieter Wolke9044426516
Paul J. Thornalley8932127613
Thomas E. Nichols8841158970
Keith R. Abrams8635530980
Alexandra M. Z. Slawin85160738583
David A. Leigh8533926392
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20224
2021378
2020289
2019253
2018224
2017240